Overview

Dataset statistics

Number of variables34
Number of observations69936
Missing cells0
Missing cells (%)0.0%
Duplicate rows7549
Duplicate rows (%)10.8%
Total size in memory124.5 MiB
Average record size in memory1.8 KiB

Variable types

CAT25
NUM8
DATE1

Warnings

Conducta has constant value "69936" Constant
Dataset has 7549 (10.8%) duplicate rows Duplicates
Barrio has a high cardinality: 329 distinct values High cardinality
nombre_barrio has a high cardinality: 329 distinct values High cardinality
Cuadrante has a high cardinality: 413 distinct values High cardinality
Lugar has a high cardinality: 77 distinct values High cardinality
Linea Marca Hurto has a high cardinality: 1042 distinct values High cardinality
Marca has a high cardinality: 79 distinct values High cardinality
Plana Y Hurto is highly correlated with Plana X Hurto and 2 other fieldsHigh correlation
Plana X Hurto is highly correlated with Plana Y Hurto and 2 other fieldsHigh correlation
Geo X Hurto is highly correlated with Plana X Hurto and 2 other fieldsHigh correlation
Geo Y Hurto is highly correlated with Plana X Hurto and 2 other fieldsHigh correlation
Categoría Bien is highly correlated with BienHigh correlation
Bien is highly correlated with Categoría Bien and 1 other fieldsHigh correlation
Grupo Bien is highly correlated with BienHigh correlation
nombre_comuna is highly correlated with ComunaHigh correlation
Comuna is highly correlated with nombre_comunaHigh correlation
Grupo Lugar is highly correlated with LugarHigh correlation
Lugar is highly correlated with Grupo LugarHigh correlation
Valor Hurto is highly skewed (γ1 = 135.0868956) Skewed

Reproduction

Analysis started2021-02-08 23:28:57.884178
Analysis finished2021-02-08 23:29:18.603729
Duration20.72 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Año
Real number (ℝ≥0)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.367465
Minimum2003
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size546.5 KiB
2021-02-08T18:29:18.644502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12009
median2013
Q32017
95-th percentile2020
Maximum2020
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.136647828
Coefficient of variation (CV)0.002552539692
Kurtosis-1.00241515
Mean2012.367465
Median Absolute Deviation (MAD)4
Skewness-0.3263261918
Sum140736931
Variance26.38515091
MonotocityIncreasing
2021-02-08T18:29:18.731810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
201651307.3%
 
201250957.3%
 
201949697.1%
 
201149477.1%
 
201349247.0%
 
201848136.9%
 
201446686.7%
 
200345166.5%
 
201543766.3%
 
202041746.0%
 
201739825.7%
 
200933414.8%
 
200431904.6%
 
200527173.9%
 
200625383.6%
 
201022873.3%
 
200721413.1%
 
200821283.0%
 
ValueCountFrequency (%) 
200345166.5%
 
200431904.6%
 
200527173.9%
 
200625383.6%
 
200721413.1%
 
200821283.0%
 
200933414.8%
 
201022873.3%
 
201149477.1%
 
201250957.3%
 
ValueCountFrequency (%) 
202041746.0%
 
201949697.1%
 
201848136.9%
 
201739825.7%
 
201651307.3%
 
201543766.3%
 
201446686.7%
 
201349247.0%
 
201250957.3%
 
201149477.1%
 

Día
Date

Distinct6434
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Minimum2003-01-01 00:00:00
Maximum2020-12-31 00:00:00
2021-02-08T18:29:18.837578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:18.941514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Día nombre
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Miércoles
11959 
Jueves
11308 
Viernes
10856 
Lunes
10190 
Martes
10010 
Other values (2)
15613 
ValueCountFrequency (%) 
Miércoles1195917.1%
 
Jueves1130816.2%
 
Viernes1085615.5%
 
Lunes1019014.6%
 
Martes1001014.3%
 
Sábado905813.0%
 
Domingo65559.4%
 
2021-02-08T18:29:19.057884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:19.126415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:19.238416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length6.616249142
Min length5

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e7648716.5%
 
s5432311.7%
 
o341277.4%
 
r328257.1%
 
i293706.3%
 
n276016.0%
 
M219694.7%
 
u214984.6%
 
a190684.1%
 
é119592.6%
 
c119592.6%
 
l119592.6%
 
J113082.4%
 
v113082.4%
 
V108562.3%
 
L101902.2%
 
t100102.2%
 
S90582.0%
 
á90582.0%
 
b90582.0%
 
d90582.0%
 
D65551.4%
 
m65551.4%
 
g65551.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter39277884.9%
 
Uppercase Letter6993615.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M2196931.4%
 
J1130816.2%
 
V1085615.5%
 
L1019014.6%
 
S905813.0%
 
D65559.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e7648719.5%
 
s5432313.8%
 
o341278.7%
 
r328258.4%
 
i293707.5%
 
n276017.0%
 
u214985.5%
 
a190684.9%
 
é119593.0%
 
c119593.0%
 
l119593.0%
 
v113082.9%
 
t100102.5%
 
á90582.3%
 
b90582.3%
 
d90582.3%
 
m65551.7%
 
g65551.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin462714100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e7648716.5%
 
s5432311.7%
 
o341277.4%
 
r328257.1%
 
i293706.3%
 
n276016.0%
 
M219694.7%
 
u214984.6%
 
a190684.1%
 
é119592.6%
 
c119592.6%
 
l119592.6%
 
J113082.4%
 
v113082.4%
 
V108562.3%
 
L101902.2%
 
t100102.2%
 
S90582.0%
 
á90582.0%
 
b90582.0%
 
d90582.0%
 
D65551.4%
 
m65551.4%
 
g65551.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII44169795.5%
 
None210174.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e7648717.3%
 
s5432312.3%
 
o341277.7%
 
r328257.4%
 
i293706.6%
 
n276016.2%
 
M219695.0%
 
u214984.9%
 
a190684.3%
 
c119592.7%
 
l119592.7%
 
J113082.6%
 
v113082.6%
 
V108562.5%
 
L101902.3%
 
t100102.3%
 
S90582.1%
 
b90582.1%
 
d90582.1%
 
D65551.5%
 
m65551.5%
 
g65551.5%
 

Most frequent None characters

ValueCountFrequency (%) 
é1195956.9%
 
á905843.1%
 

Mes Calendario
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Mayo
6211 
Agosto
6163 
Septiembre
6069 
Octubre
6051 
Enero
5980 
Other values (7)
39462 
ValueCountFrequency (%) 
Mayo62118.9%
 
Agosto61638.8%
 
Septiembre60698.7%
 
Octubre60518.7%
 
Enero59808.6%
 
Julio59638.5%
 
Febrero58588.4%
 
Marzo57888.3%
 
Noviembre56048.0%
 
Junio55347.9%
 
Abril54477.8%
 
Diciembre52687.5%
 
2021-02-08T18:29:19.343783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:19.439408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length6
Mean length6.395604553
Min length4

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e6369814.2%
 
o5326411.9%
 
r5192311.6%
 
i391538.8%
 
b342977.7%
 
t182834.1%
 
u175483.9%
 
m169413.8%
 
M119992.7%
 
a119992.7%
 
A116102.6%
 
n115142.6%
 
J114972.6%
 
l114102.6%
 
c113192.5%
 
y62111.4%
 
g61631.4%
 
s61631.4%
 
S60691.4%
 
p60691.4%
 
O60511.4%
 
E59801.3%
 
F58581.3%
 
z57881.3%
 
N56041.3%
 
Other values (2)108722.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter37734784.4%
 
Uppercase Letter6993615.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M1199917.2%
 
A1161016.6%
 
J1149716.4%
 
S60698.7%
 
O60518.7%
 
E59808.6%
 
F58588.4%
 
N56048.0%
 
D52687.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e6369816.9%
 
o5326414.1%
 
r5192313.8%
 
i3915310.4%
 
b342979.1%
 
t182834.8%
 
u175484.7%
 
m169414.5%
 
a119993.2%
 
n115143.1%
 
l114103.0%
 
c113193.0%
 
y62111.6%
 
g61631.6%
 
s61631.6%
 
p60691.6%
 
z57881.5%
 
v56041.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin447283100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e6369814.2%
 
o5326411.9%
 
r5192311.6%
 
i391538.8%
 
b342977.7%
 
t182834.1%
 
u175483.9%
 
m169413.8%
 
M119992.7%
 
a119992.7%
 
A116102.6%
 
n115142.6%
 
J114972.6%
 
l114102.6%
 
c113192.5%
 
y62111.4%
 
g61631.4%
 
s61631.4%
 
S60691.4%
 
p60691.4%
 
O60511.4%
 
E59801.3%
 
F58581.3%
 
z57881.3%
 
N56041.3%
 
Other values (2)108722.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII447283100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e6369814.2%
 
o5326411.9%
 
r5192311.6%
 
i391538.8%
 
b342977.7%
 
t182834.1%
 
u175483.9%
 
m169413.8%
 
M119992.7%
 
a119992.7%
 
A116102.6%
 
n115142.6%
 
J114972.6%
 
l114102.6%
 
c113192.5%
 
y62111.4%
 
g61631.4%
 
s61631.4%
 
S60691.4%
 
p60691.4%
 
O60511.4%
 
E59801.3%
 
F58581.3%
 
z57881.3%
 
N56041.3%
 
Other values (2)108722.4%
 

Jornada
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Noche
27324 
Tarde
15577 
Mañana
14173 
Madrugada
12862 
ValueCountFrequency (%) 
Noche2732439.1%
 
Tarde1557722.3%
 
Mañana1417320.3%
 
Madrugada1286218.4%
 
2021-02-08T18:29:19.652475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:19.727980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:19.809485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length5.938300732
Min length5

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a9668223.3%
 
e4290110.3%
 
d413019.9%
 
r284396.8%
 
N273246.6%
 
o273246.6%
 
c273246.6%
 
h273246.6%
 
M270356.5%
 
T155773.8%
 
ñ141733.4%
 
n141733.4%
 
u128623.1%
 
g128623.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter34536583.2%
 
Uppercase Letter6993616.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2732439.1%
 
M2703538.7%
 
T1557722.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a9668228.0%
 
e4290112.4%
 
d4130112.0%
 
r284398.2%
 
o273247.9%
 
c273247.9%
 
h273247.9%
 
ñ141734.1%
 
n141734.1%
 
u128623.7%
 
g128623.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin415301100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a9668223.3%
 
e4290110.3%
 
d413019.9%
 
r284396.8%
 
N273246.6%
 
o273246.6%
 
c273246.6%
 
h273246.6%
 
M270356.5%
 
T155773.8%
 
ñ141733.4%
 
n141733.4%
 
u128623.1%
 
g128623.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII40112896.6%
 
None141733.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a9668224.1%
 
e4290110.7%
 
d4130110.3%
 
r284397.1%
 
N273246.8%
 
o273246.8%
 
c273246.8%
 
h273246.8%
 
M270356.7%
 
T155773.9%
 
n141733.5%
 
u128623.2%
 
g128623.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ñ14173100.0%
 

Bien
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Moto
69874 
Moto carro
 
55
Cuatrimoto
 
6
Carne
 
1
ValueCountFrequency (%) 
Moto6987499.9%
 
Moto carro550.1%
 
Cuatrimoto6< 0.1%
 
Carne1< 0.1%
 
2021-02-08T18:29:19.909506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-02-08T18:29:19.974783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:20.054562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length4
Mean length4.005247655
Min length4

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o13992550.0%
 
t6994125.0%
 
M6992925.0%
 
r117< 0.1%
 
a62< 0.1%
 
55< 0.1%
 
c55< 0.1%
 
C7< 0.1%
 
u6< 0.1%
 
i6< 0.1%
 
m6< 0.1%
 
n1< 0.1%
 
e1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter21012075.0%
 
Uppercase Letter6993625.0%
 
Space Separator55< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M69929> 99.9%
 
C7< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o13992566.6%
 
t6994133.3%
 
r1170.1%
 
a62< 0.1%
 
c55< 0.1%
 
u6< 0.1%
 
i6< 0.1%
 
m6< 0.1%
 
n1< 0.1%
 
e1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
55100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin280056> 99.9%
 
Common55< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o13992550.0%
 
t6994125.0%
 
M6992925.0%
 
r117< 0.1%
 
a62< 0.1%
 
c55< 0.1%
 
C7< 0.1%
 
u6< 0.1%
 
i6< 0.1%
 
m6< 0.1%
 
n1< 0.1%
 
e1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
55100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII280111100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o13992550.0%
 
t6994125.0%
 
M6992925.0%
 
r117< 0.1%
 
a62< 0.1%
 
55< 0.1%
 
c55< 0.1%
 
C7< 0.1%
 
u6< 0.1%
 
i6< 0.1%
 
m6< 0.1%
 
n1< 0.1%
 
e1< 0.1%
 

Categoría Bien
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Vehículos de 2 o 4 ruedas
69935 
Alimento
 
1
ValueCountFrequency (%) 
Vehículos de 2 o 4 ruedas69935> 99.9%
 
Alimento1< 0.1%
 
2021-02-08T18:29:20.154827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-02-08T18:29:20.220284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:20.294896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length25
Mean length24.99975692
Min length8

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
34967520.0%
 
e20980612.0%
 
o1398718.0%
 
u1398708.0%
 
s1398708.0%
 
d1398708.0%
 
l699364.0%
 
V699354.0%
 
h699354.0%
 
í699354.0%
 
c699354.0%
 
2699354.0%
 
4699354.0%
 
r699354.0%
 
a699354.0%
 
A1< 0.1%
 
i1< 0.1%
 
m1< 0.1%
 
n1< 0.1%
 
t1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter118890268.0%
 
Space Separator34967520.0%
 
Decimal Number1398708.0%
 
Uppercase Letter699364.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V69935> 99.9%
 
A1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e20980617.6%
 
o13987111.8%
 
u13987011.8%
 
s13987011.8%
 
d13987011.8%
 
l699365.9%
 
h699355.9%
 
í699355.9%
 
c699355.9%
 
r699355.9%
 
a699355.9%
 
i1< 0.1%
 
m1< 0.1%
 
n1< 0.1%
 
t1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
349675100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
26993550.0%
 
46993550.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin125883872.0%
 
Common48954528.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e20980616.7%
 
o13987111.1%
 
u13987011.1%
 
s13987011.1%
 
d13987011.1%
 
l699365.6%
 
V699355.6%
 
h699355.6%
 
í699355.6%
 
c699355.6%
 
r699355.6%
 
a699355.6%
 
A1< 0.1%
 
i1< 0.1%
 
m1< 0.1%
 
n1< 0.1%
 
t1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
34967571.4%
 
26993514.3%
 
46993514.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII167844896.0%
 
None699354.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
34967520.8%
 
e20980612.5%
 
o1398718.3%
 
u1398708.3%
 
s1398708.3%
 
d1398708.3%
 
l699364.2%
 
V699354.2%
 
h699354.2%
 
c699354.2%
 
2699354.2%
 
4699354.2%
 
r699354.2%
 
a699354.2%
 
A1< 0.1%
 
i1< 0.1%
 
m1< 0.1%
 
n1< 0.1%
 
t1< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
í69935100.0%
 

Grupo Bien
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Vehículo
69935 
Mercancía
 
1
ValueCountFrequency (%) 
Vehículo69935> 99.9%
 
Mercancía1< 0.1%
 
2021-02-08T18:29:20.393512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-02-08T18:29:20.454998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:20.521908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length8.000014299
Min length8

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
c6993712.5%
 
e6993612.5%
 
í6993612.5%
 
V6993512.5%
 
h6993512.5%
 
u6993512.5%
 
l6993512.5%
 
o6993512.5%
 
a2< 0.1%
 
M1< 0.1%
 
r1< 0.1%
 
n1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter48955387.5%
 
Uppercase Letter6993612.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V69935> 99.9%
 
M1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
c6993714.3%
 
e6993614.3%
 
í6993614.3%
 
h6993514.3%
 
u6993514.3%
 
l6993514.3%
 
o6993514.3%
 
a2< 0.1%
 
r1< 0.1%
 
n1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin559489100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
c6993712.5%
 
e6993612.5%
 
í6993612.5%
 
V6993512.5%
 
h6993512.5%
 
u6993512.5%
 
l6993512.5%
 
o6993512.5%
 
a2< 0.1%
 
M1< 0.1%
 
r1< 0.1%
 
n1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII48955387.5%
 
None6993612.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
c6993714.3%
 
e6993614.3%
 
V6993514.3%
 
h6993514.3%
 
u6993514.3%
 
l6993514.3%
 
o6993514.3%
 
a2< 0.1%
 
M1< 0.1%
 
r1< 0.1%
 
n1< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
í69936100.0%
 

Conducta Especial
Categorical

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Sin dato
38450 
No
29801 
Grupo delincuencial
 
1497
De celular
 
70
Piratería
 
26
Other values (23)
 
92
ValueCountFrequency (%) 
Sin dato3845055.0%
 
No2980142.6%
 
Grupo delincuencial14972.1%
 
De celular700.1%
 
Piratería26< 0.1%
 
Fleteo24< 0.1%
 
Hostigamiento9< 0.1%
 
De cable8< 0.1%
 
Extorsión7< 0.1%
 
Falsificación5< 0.1%
 
A bus de servicio público5< 0.1%
 
Muerte o lesión de delincuente4< 0.1%
 
A vehículo repartidor3< 0.1%
 
De Hidrocarburo3< 0.1%
 
Paseo millonario2< 0.1%
 
Desplazamiento forzado2< 0.1%
 
Plaza de vicio2< 0.1%
 
Violencia contra la mujer2< 0.1%
 
Paro o protesta2< 0.1%
 
Voladura de infraestructura pública2< 0.1%
 
Medios informáticos2< 0.1%
 
Incursión2< 0.1%
 
De Aeronave2< 0.1%
 
A taxista2< 0.1%
 
Pornografía infantil1< 0.1%
 
Other values (3)3< 0.1%
 
2021-02-08T18:29:20.627961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2021-02-08T18:29:20.750190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length8
Mean length5.68785747
Min length2

Overview of Unicode Properties

Unique unicode characters40
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o6986117.6%
 
i4157410.5%
 
n4149910.4%
 
a4014910.1%
 
4010110.1%
 
d3997810.1%
 
t385569.7%
 
S384509.7%
 
N298017.5%
 
e33080.8%
 
l32060.8%
 
c31190.8%
 
u30940.8%
 
r16840.4%
 
p15110.4%
 
G14970.4%
 
D85< 0.1%
 
s51< 0.1%
 
P33< 0.1%
 
í30< 0.1%
 
F29< 0.1%
 
b23< 0.1%
 
ó19< 0.1%
 
m18< 0.1%
 
f13< 0.1%
 
Other values (15)97< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter28774472.3%
 
Uppercase Letter6994117.6%
 
Space Separator4010110.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S3845055.0%
 
N2980142.6%
 
G14972.1%
 
D850.1%
 
P33< 0.1%
 
F29< 0.1%
 
A13< 0.1%
 
H13< 0.1%
 
E7< 0.1%
 
M6< 0.1%
 
V4< 0.1%
 
I2< 0.1%
 
C1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o6986124.3%
 
i4157414.4%
 
n4149914.4%
 
a4014914.0%
 
d3997813.9%
 
t3855613.4%
 
e33081.1%
 
l32061.1%
 
c31191.1%
 
u30941.1%
 
r16840.6%
 
p15110.5%
 
s51< 0.1%
 
í30< 0.1%
 
b23< 0.1%
 
ó19< 0.1%
 
m18< 0.1%
 
f13< 0.1%
 
v12< 0.1%
 
g10< 0.1%
 
x9< 0.1%
 
ú7< 0.1%
 
z6< 0.1%
 
h3< 0.1%
 
á2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
40101100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin35768589.9%
 
Common4010110.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o6986119.5%
 
i4157411.6%
 
n4149911.6%
 
a4014911.2%
 
d3997811.2%
 
t3855610.8%
 
S3845010.7%
 
N298018.3%
 
e33080.9%
 
l32060.9%
 
c31190.9%
 
u30940.9%
 
r16840.5%
 
p15110.4%
 
G14970.4%
 
D85< 0.1%
 
s51< 0.1%
 
P33< 0.1%
 
í30< 0.1%
 
F29< 0.1%
 
b23< 0.1%
 
ó19< 0.1%
 
m18< 0.1%
 
f13< 0.1%
 
A13< 0.1%
 
Other values (14)84< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
40101100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII397728> 99.9%
 
None58< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o6986117.6%
 
i4157410.5%
 
n4149910.4%
 
a4014910.1%
 
4010110.1%
 
d3997810.1%
 
t385569.7%
 
S384509.7%
 
N298017.5%
 
e33080.8%
 
l32060.8%
 
c31190.8%
 
u30940.8%
 
r16840.4%
 
p15110.4%
 
G14970.4%
 
D85< 0.1%
 
s51< 0.1%
 
P33< 0.1%
 
F29< 0.1%
 
b23< 0.1%
 
m18< 0.1%
 
f13< 0.1%
 
A13< 0.1%
 
H13< 0.1%
 
Other values (11)62< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
í3051.7%
 
ó1932.8%
 
ú712.1%
 
á23.4%
 

Conducta
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Hurto de moto
69936 
ValueCountFrequency (%) 
Hurto de moto69936100.0%
 
2021-02-08T18:29:20.853572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:20.913652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:20.972937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length13
Min length13

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o20980823.1%
 
t13987215.4%
 
13987215.4%
 
H699367.7%
 
u699367.7%
 
r699367.7%
 
d699367.7%
 
e699367.7%
 
m699367.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter69936076.9%
 
Space Separator13987215.4%
 
Uppercase Letter699367.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H69936100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o20980830.0%
 
t13987220.0%
 
u6993610.0%
 
r6993610.0%
 
d6993610.0%
 
e6993610.0%
 
m6993610.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
139872100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin76929684.6%
 
Common13987215.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o20980827.3%
 
t13987218.2%
 
H699369.1%
 
u699369.1%
 
r699369.1%
 
d699369.1%
 
e699369.1%
 
m699369.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
139872100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII909168100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o20980823.1%
 
t13987215.4%
 
13987215.4%
 
H699367.7%
 
u699367.7%
 
r699367.7%
 
d699367.7%
 
e699367.7%
 
m699367.7%
 

Modalidad
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Halado
37544 
Atraco
26662 
Sin dato
4368 
Engaño
 
573
Descuido
 
440
Other values (10)
 
349
ValueCountFrequency (%) 
Halado3754453.7%
 
Atraco2666238.1%
 
Sin dato43686.2%
 
Engaño5730.8%
 
Descuido4400.6%
 
Escopolamina1360.2%
 
Llave maestra1350.2%
 
Abuso de confianza480.1%
 
Miedo o terror13< 0.1%
 
Suplantación5< 0.1%
 
Tóxico o agente químico5< 0.1%
 
Desvalijar o descuartizar2< 0.1%
 
Taquillazo2< 0.1%
 
Enfrentamiento con la fuerza pública2< 0.1%
 
Vehículo1< 0.1%
 
2021-02-08T18:29:21.070229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-02-08T18:29:21.172947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length6
Mean length6.17558911
Min length6

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a10749924.9%
 
o7001816.2%
 
d424159.8%
 
l378318.8%
 
H375448.7%
 
t311947.2%
 
c273086.3%
 
r268466.2%
 
A267106.2%
 
n51961.2%
 
i50301.2%
 
46521.1%
 
S43731.0%
 
e8050.2%
 
s7630.2%
 
E7110.2%
 
g5780.1%
 
ñ5730.1%
 
u5050.1%
 
D4420.1%
 
m2780.1%
 
p143< 0.1%
 
v137< 0.1%
 
L135< 0.1%
 
z54< 0.1%
 
Other values (12)156< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter35730882.7%
 
Uppercase Letter6993616.2%
 
Space Separator46521.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H3754453.7%
 
A2671038.2%
 
S43736.3%
 
E7111.0%
 
D4420.6%
 
L1350.2%
 
M13< 0.1%
 
T7< 0.1%
 
V1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a10749930.1%
 
o7001819.6%
 
d4241511.9%
 
l3783110.6%
 
t311948.7%
 
c273087.6%
 
r268467.5%
 
n51961.5%
 
i50301.4%
 
e8050.2%
 
s7630.2%
 
g5780.2%
 
ñ5730.2%
 
u5050.1%
 
m2780.1%
 
p143< 0.1%
 
v137< 0.1%
 
z54< 0.1%
 
f52< 0.1%
 
b50< 0.1%
 
ó10< 0.1%
 
q7< 0.1%
 
í6< 0.1%
 
x5< 0.1%
 
j2< 0.1%
 
Other values (2)3< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4652100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin42724498.9%
 
Common46521.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a10749925.2%
 
o7001816.4%
 
d424159.9%
 
l378318.9%
 
H375448.8%
 
t311947.3%
 
c273086.4%
 
r268466.3%
 
A267106.3%
 
n51961.2%
 
i50301.2%
 
S43731.0%
 
e8050.2%
 
s7630.2%
 
E7110.2%
 
g5780.1%
 
ñ5730.1%
 
u5050.1%
 
D4420.1%
 
m2780.1%
 
p143< 0.1%
 
v137< 0.1%
 
L135< 0.1%
 
z54< 0.1%
 
f52< 0.1%
 
Other values (11)104< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
4652100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII43130599.9%
 
None5910.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a10749924.9%
 
o7001816.2%
 
d424159.8%
 
l378318.8%
 
H375448.7%
 
t311947.2%
 
c273086.3%
 
r268466.2%
 
A267106.2%
 
n51961.2%
 
i50301.2%
 
46521.1%
 
S43731.0%
 
e8050.2%
 
s7630.2%
 
E7110.2%
 
g5780.1%
 
u5050.1%
 
D4420.1%
 
m2780.1%
 
p143< 0.1%
 
v137< 0.1%
 
L135< 0.1%
 
z54< 0.1%
 
f52< 0.1%
 
Other values (8)86< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ñ57397.0%
 
ó101.7%
 
í61.0%
 
ú20.3%
 

Sexo
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Hombre
56784 
Mujer
11492 
Sin dato
 
1660
ValueCountFrequency (%) 
Hombre5678481.2%
 
Mujer1149216.4%
 
Sin dato16602.4%
 
2021-02-08T18:29:21.272817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:21.334009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:21.405622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length6
Mean length5.883150309
Min length5

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r6827616.6%
 
e6827616.6%
 
o5844414.2%
 
H5678413.8%
 
m5678413.8%
 
b5678413.8%
 
M114922.8%
 
u114922.8%
 
j114922.8%
 
S16600.4%
 
i16600.4%
 
n16600.4%
 
16600.4%
 
d16600.4%
 
a16600.4%
 
t16600.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter33984882.6%
 
Uppercase Letter6993617.0%
 
Space Separator16600.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H5678481.2%
 
M1149216.4%
 
S16602.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r6827620.1%
 
e6827620.1%
 
o5844417.2%
 
m5678416.7%
 
b5678416.7%
 
u114923.4%
 
j114923.4%
 
i16600.5%
 
n16600.5%
 
d16600.5%
 
a16600.5%
 
t16600.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1660100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin40978499.6%
 
Common16600.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r6827616.7%
 
e6827616.7%
 
o5844414.3%
 
H5678413.9%
 
m5678413.9%
 
b5678413.9%
 
M114922.8%
 
u114922.8%
 
j114922.8%
 
S16600.4%
 
i16600.4%
 
n16600.4%
 
d16600.4%
 
a16600.4%
 
t16600.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
1660100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII411444100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r6827616.6%
 
e6827616.6%
 
o5844414.2%
 
H5678413.8%
 
m5678413.8%
 
b5678413.8%
 
M114922.8%
 
u114922.8%
 
j114922.8%
 
S16600.4%
 
i16600.4%
 
n16600.4%
 
16600.4%
 
d16600.4%
 
a16600.4%
 
t16600.4%
 

Edad Hurto
Real number (ℝ)

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.59937657
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Memory size546.5 KiB
2021-02-08T18:29:21.506541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile18
Q123
median28
Q335
95-th percentile48
Maximum99
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.27482048
Coefficient of variation (CV)0.3471296246
Kurtosis1.95284098
Mean29.59937657
Median Absolute Deviation (MAD)5
Skewness0.2414240547
Sum2070062
Variance105.5719358
MonotocityNot monotonic
2021-02-08T18:29:21.622975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2545626.5%
 
2436105.2%
 
2635895.1%
 
2335865.1%
 
2735205.0%
 
2233364.8%
 
2832614.7%
 
3031034.4%
 
2130174.3%
 
2929464.2%
 
2026613.8%
 
3224363.5%
 
3123543.4%
 
3521633.1%
 
3321483.1%
 
3418932.7%
 
-118272.6%
 
1917772.5%
 
3616712.4%
 
3714752.1%
 
3814622.1%
 
3912761.8%
 
4011381.6%
 
1810811.5%
 
419841.4%
 
Other values (57)906013.0%
 
ValueCountFrequency (%) 
-118272.6%
 
113< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
62< 0.1%
 
83< 0.1%
 
102< 0.1%
 
112< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
994< 0.1%
 
952< 0.1%
 
941< 0.1%
 
881< 0.1%
 
811< 0.1%
 
791< 0.1%
 
781< 0.1%
 
772< 0.1%
 
763< 0.1%
 
755< 0.1%
 

Rango Edad
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
18-24
19068 
25-28
14932 
29-32
10839 
33-38
10812 
39-45
6604 
Other values (9)
7681 
ValueCountFrequency (%) 
18-241906827.3%
 
25-281493221.4%
 
29-321083915.5%
 
33-381081215.5%
 
39-4566049.4%
 
46-5233724.8%
 
Sin dato18272.6%
 
53-5913712.0%
 
14-176070.9%
 
60-663760.5%
 
Mayor de 671000.1%
 
0-516< 0.1%
 
6-119< 0.1%
 
12-133< 0.1%
 
2021-02-08T18:29:21.852981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:21.957572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length5
Mean length5.086364676
Min length3

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
27398520.8%
 
-6800919.1%
 
35125314.4%
 
84481212.6%
 
4296518.3%
 
5276667.8%
 
1203065.7%
 
9188145.3%
 
646091.3%
 
20270.6%
 
d19270.5%
 
a19270.5%
 
o19270.5%
 
S18270.5%
 
i18270.5%
 
n18270.5%
 
t18270.5%
 
77070.2%
 
03920.1%
 
M100< 0.1%
 
y100< 0.1%
 
r100< 0.1%
 
e100< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number27219576.5%
 
Dash Punctuation6800919.1%
 
Lowercase Letter115623.3%
 
Space Separator20270.6%
 
Uppercase Letter19270.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
27398527.2%
 
35125318.8%
 
84481216.5%
 
42965110.9%
 
52766610.2%
 
1203067.5%
 
9188146.9%
 
646091.7%
 
77070.3%
 
03920.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-68009100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S182794.8%
 
M1005.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d192716.7%
 
a192716.7%
 
o192716.7%
 
i182715.8%
 
n182715.8%
 
t182715.8%
 
y1000.9%
 
r1000.9%
 
e1000.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2027100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common34223196.2%
 
Latin134893.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
27398521.6%
 
-6800919.9%
 
35125315.0%
 
84481213.1%
 
4296518.7%
 
5276668.1%
 
1203065.9%
 
9188145.5%
 
646091.3%
 
20270.6%
 
77070.2%
 
03920.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d192714.3%
 
a192714.3%
 
o192714.3%
 
S182713.5%
 
i182713.5%
 
n182713.5%
 
t182713.5%
 
M1000.7%
 
y1000.7%
 
r1000.7%
 
e1000.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII355720100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
27398520.8%
 
-6800919.1%
 
35125314.4%
 
84481212.6%
 
4296518.3%
 
5276667.8%
 
1203065.7%
 
9188145.3%
 
646091.3%
 
20270.6%
 
d19270.5%
 
a19270.5%
 
o19270.5%
 
S18270.5%
 
i18270.5%
 
n18270.5%
 
t18270.5%
 
77070.2%
 
03920.1%
 
M100< 0.1%
 
y100< 0.1%
 
r100< 0.1%
 
e100< 0.1%
 

Barrio
Categorical

HIGH CARDINALITY

Distinct329
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
#1001
 
2138
#1016
 
1616
#1019
 
1550
#0409
 
1108
#0511
 
970
Other values (324)
62554 
ValueCountFrequency (%) 
#100121383.1%
 
#101616162.3%
 
#101915502.2%
 
#040911081.6%
 
#05119701.4%
 
#09079121.3%
 
#05179011.3%
 
#08038821.3%
 
#16038351.2%
 
#03078231.2%
 
#10178151.2%
 
#10187981.1%
 
#04107921.1%
 
SIN DATO7841.1%
 
#04137521.1%
 
#07177471.1%
 
#10047191.0%
 
#10067171.0%
 
#15076080.9%
 
#04055680.8%
 
#10125630.8%
 
#15105630.8%
 
#11085620.8%
 
#10155610.8%
 
#04075600.8%
 
Other values (304)4809268.8%
 
2021-02-08T18:29:22.073753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2021-02-08T18:29:22.181854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length5
Mean length5.108055937
Min length4

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
08977325.1%
 
17439120.8%
 
#6913719.4%
 
4150734.2%
 
5145674.1%
 
7140893.9%
 
3131263.7%
 
2128043.6%
 
6127523.6%
 
9121763.4%
 
895502.7%
 
I29260.8%
 
n21150.6%
 
s21150.6%
 
t21150.6%
 
_21150.6%
 
A18820.5%
 
8430.2%
 
N8140.2%
 
S8120.2%
 
O8110.2%
 
D8080.2%
 
T8070.2%
 
C5390.2%
 
U5290.1%
 
Other values (6)5680.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number26830175.1%
 
Other Punctuation6913719.4%
 
Uppercase Letter104962.9%
 
Lowercase Letter63451.8%
 
Connector Punctuation21150.6%
 
Space Separator8430.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
#69137100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
08977333.5%
 
17439127.7%
 
4150735.6%
 
5145675.4%
 
7140895.3%
 
3131264.9%
 
2128044.8%
 
6127524.8%
 
9121764.5%
 
895503.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I292627.9%
 
A188217.9%
 
N8147.8%
 
S8127.7%
 
O8117.7%
 
D8087.7%
 
T8077.7%
 
C5395.1%
 
U5295.0%
 
E5265.0%
 
L190.2%
 
R130.1%
 
B70.1%
 
P2< 0.1%
 
M1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n211533.3%
 
s211533.3%
 
t211533.3%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_2115100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
843100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common34039695.3%
 
Latin168414.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
08977326.4%
 
17439121.9%
 
#6913720.3%
 
4150734.4%
 
5145674.3%
 
7140894.1%
 
3131263.9%
 
2128043.8%
 
6127523.7%
 
9121763.6%
 
895502.8%
 
_21150.6%
 
8430.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I292617.4%
 
n211512.6%
 
s211512.6%
 
t211512.6%
 
A188211.2%
 
N8144.8%
 
S8124.8%
 
O8114.8%
 
D8084.8%
 
T8074.8%
 
C5393.2%
 
U5293.1%
 
E5263.1%
 
L190.1%
 
R130.1%
 
B7< 0.1%
 
P2< 0.1%
 
M1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII357237100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
08977325.1%
 
17439120.8%
 
#6913719.4%
 
4150734.2%
 
5145674.1%
 
7140893.9%
 
3131263.7%
 
2128043.6%
 
6127523.6%
 
9121763.4%
 
895502.7%
 
I29260.8%
 
n21150.6%
 
s21150.6%
 
t21150.6%
 
_21150.6%
 
A18820.5%
 
8430.2%
 
N8140.2%
 
S8120.2%
 
O8110.2%
 
D8080.2%
 
T8070.2%
 
C5390.2%
 
U5290.1%
 
Other values (6)5680.2%
 

nombre_barrio
Categorical

HIGH CARDINALITY

Distinct329
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Prado
 
2138
Boston
 
1616
La Candelaria
 
1550
Manrique Central No.1
 
1108
Castilla
 
970
Other values (324)
62554 
ValueCountFrequency (%) 
Prado21383.1%
 
Boston16162.3%
 
La Candelaria15502.2%
 
Manrique Central No.111081.6%
 
Castilla9701.4%
 
Buenos Aires9121.3%
 
Caribe9011.3%
 
San Miguel8821.3%
 
Belén8351.2%
 
Manrique Central No.28231.2%
 
Los Ángeles8151.2%
 
Villa Nueva7981.1%
 
Campo Valdés No.17921.1%
 
Sin dato7841.1%
 
Aranjuez7521.1%
 
Robledo7471.1%
 
El Chagualo7191.0%
 
San Benito7171.0%
 
Campo Amor6080.9%
 
Moravia5680.8%
 
Guayabal5630.8%
 
Perpetuo Socorro5630.8%
 
Laureles5620.8%
 
Bomboná No.15610.8%
 
Sevilla5600.8%
 
Other values (304)4809268.8%
 
2021-02-08T18:29:22.294310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2021-02-08T18:29:22.408147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length10
Mean length11.44506406
Min length4

Overview of Unicode Properties

Unique unicode characters68
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a10840913.5%
 
o638338.0%
 
615117.7%
 
e561447.0%
 
r557767.0%
 
l465415.8%
 
n463045.8%
 
i416055.2%
 
s355704.4%
 
t237343.0%
 
d230582.9%
 
u169372.1%
 
C161672.0%
 
L152371.9%
 
c122881.5%
 
S118331.5%
 
B107371.3%
 
P102181.3%
 
m98331.2%
 
N97231.2%
 
M84301.1%
 
A82911.0%
 
.81621.0%
 
b69440.9%
 
E67600.8%
 
Other values (43)8637710.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter59842474.8%
 
Uppercase Letter12576015.7%
 
Space Separator615117.7%
 
Other Punctuation81621.0%
 
Decimal Number61930.8%
 
Dash Punctuation372< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1616712.9%
 
L1523712.1%
 
S118339.4%
 
B107378.5%
 
P102188.1%
 
N97237.7%
 
M84306.7%
 
A82916.6%
 
E67605.4%
 
V52154.1%
 
G33832.7%
 
F30382.4%
 
R27612.2%
 
T22631.8%
 
J20381.6%
 
D19981.6%
 
Á19351.5%
 
U14881.2%
 
I13241.1%
 
H11020.9%
 
O10460.8%
 
K3630.3%
 
Z2480.2%
 
X1040.1%
 
Q52< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a10840918.1%
 
o6383310.7%
 
e561449.4%
 
r557769.3%
 
l465417.8%
 
n463047.7%
 
i416057.0%
 
s355705.9%
 
t237344.0%
 
d230583.9%
 
u169372.8%
 
c122882.1%
 
m98331.6%
 
b69441.2%
 
g64871.1%
 
v57291.0%
 
p56190.9%
 
ó51980.9%
 
q45500.8%
 
z45450.8%
 
é34920.6%
 
j33030.6%
 
y30290.5%
 
á25110.4%
 
h20820.3%
 
Other values (5)49030.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
61511100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.8162100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1381361.6%
 
2235438.0%
 
0130.2%
 
650.1%
 
33< 0.1%
 
72< 0.1%
 
91< 0.1%
 
51< 0.1%
 
81< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-372100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin72418490.5%
 
Common762389.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a10840915.0%
 
o638338.8%
 
e561447.8%
 
r557767.7%
 
l465416.4%
 
n463046.4%
 
i416055.7%
 
s355704.9%
 
t237343.3%
 
d230583.2%
 
u169372.3%
 
C161672.2%
 
L152372.1%
 
c122881.7%
 
S118331.6%
 
B107371.5%
 
P102181.4%
 
m98331.4%
 
N97231.3%
 
M84301.2%
 
A82911.1%
 
b69441.0%
 
E67600.9%
 
g64870.9%
 
v57290.8%
 
Other values (31)675969.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
6151180.7%
 
.816210.7%
 
138135.0%
 
223543.1%
 
-3720.5%
 
013< 0.1%
 
65< 0.1%
 
33< 0.1%
 
72< 0.1%
 
91< 0.1%
 
51< 0.1%
 
81< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII78371997.9%
 
None167032.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a10840913.8%
 
o638338.1%
 
615117.8%
 
e561447.2%
 
r557767.1%
 
l465415.9%
 
n463045.9%
 
i416055.3%
 
s355704.5%
 
t237343.0%
 
d230582.9%
 
u169372.2%
 
C161672.1%
 
L152371.9%
 
c122881.6%
 
S118331.5%
 
B107371.4%
 
P102181.3%
 
m98331.3%
 
N97231.2%
 
M84301.1%
 
A82911.1%
 
.81621.0%
 
b69440.9%
 
E67600.9%
 
Other values (36)696748.9%
 

Most frequent None characters

ValueCountFrequency (%) 
ó519831.1%
 
é349220.9%
 
á251115.0%
 
Á193511.6%
 
í167010.0%
 
ú12817.7%
 
ñ6163.7%
 

Comuna
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
10
12776 
4
6977 
7
6164 
5
6027 
11
5204 
Other values (17)
32788 
ValueCountFrequency (%) 
101277618.3%
 
4697710.0%
 
761648.8%
 
560278.6%
 
1152047.4%
 
1650797.3%
 
948917.0%
 
836245.2%
 
1233364.8%
 
331194.5%
 
1531014.4%
 
622763.3%
 
1418302.6%
 
1315622.2%
 
19141.3%
 
608161.2%
 
SIN DATO7841.1%
 
27541.1%
 
804460.6%
 
901350.2%
 
701050.2%
 
5016< 0.1%
 
2021-02-08T18:29:22.528311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:22.635741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length2
Mean length1.570435827
Min length1

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
13900635.5%
 
01429413.0%
 
591448.3%
 
488078.0%
 
681717.4%
 
762695.7%
 
950264.6%
 
346814.3%
 
240903.7%
 
840703.7%
 
S7840.7%
 
I7840.7%
 
N7840.7%
 
7840.7%
 
D7840.7%
 
A7840.7%
 
T7840.7%
 
O7840.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number10355894.3%
 
Uppercase Letter54885.0%
 
Space Separator7840.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
13900637.7%
 
01429413.8%
 
591448.8%
 
488078.5%
 
681717.9%
 
762696.1%
 
950264.9%
 
346814.5%
 
240903.9%
 
840703.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S78414.3%
 
I78414.3%
 
N78414.3%
 
D78414.3%
 
A78414.3%
 
T78414.3%
 
O78414.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
784100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common10434295.0%
 
Latin54885.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
13900637.4%
 
01429413.7%
 
591448.8%
 
488078.4%
 
681717.8%
 
762696.0%
 
950264.8%
 
346814.5%
 
240903.9%
 
840703.9%
 
7840.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S78414.3%
 
I78414.3%
 
N78414.3%
 
D78414.3%
 
A78414.3%
 
T78414.3%
 
O78414.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII109830100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
13900635.5%
 
01429413.0%
 
591448.3%
 
488078.0%
 
681717.4%
 
762695.7%
 
950264.6%
 
346814.3%
 
240903.7%
 
840703.7%
 
S7840.7%
 
I7840.7%
 
N7840.7%
 
7840.7%
 
D7840.7%
 
A7840.7%
 
T7840.7%
 
O7840.7%
 

nombre_comuna
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
10 La Candelaria
12776 
4 Aranjuez
6977 
7 Robledo
6164 
5 Castilla
6027 
11 Laureles - Estadio
5204 
Other values (17)
32788 
ValueCountFrequency (%) 
10 La Candelaria1277618.3%
 
4 Aranjuez697710.0%
 
7 Robledo61648.8%
 
5 Castilla60278.6%
 
11 Laureles - Estadio52047.4%
 
16 Belen50797.3%
 
9 Buenos Aires48917.0%
 
8 Villa Hermosa36245.2%
 
12 La America33364.8%
 
3 Manrique31194.5%
 
15 Guayabal31014.4%
 
6 Doce de Octubre22763.3%
 
14 El Poblado18302.6%
 
13 San Javier15622.2%
 
1 Popular9141.3%
 
60 San Cristobal8161.2%
 
Sin dato7841.1%
 
2 Santa Cruz7541.1%
 
80 San Antonio de Prado4460.6%
 
90 Santa Elena1350.2%
 
70 Altavista1050.2%
 
50 Palmitas16< 0.1%
 
2021-02-08T18:29:22.752764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:22.864984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length13
Mean length13.00516186
Min length8

Overview of Unicode Properties

Unique unicode characters47
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
12116213.3%
 
a11821613.0%
 
e753158.3%
 
l572726.3%
 
r466955.1%
 
i427064.7%
 
1390064.3%
 
n383664.2%
 
o358353.9%
 
s307783.4%
 
d299263.3%
 
u272363.0%
 
L213162.3%
 
C203732.2%
 
t166681.8%
 
A157551.7%
 
0142941.6%
 
b141871.6%
 
B99701.1%
 
591441.0%
 
488071.0%
 
681710.9%
 
c78880.9%
 
z77310.9%
 
E71690.8%
 
Other values (22)855439.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter57157362.8%
 
Space Separator12116213.3%
 
Uppercase Letter10803211.9%
 
Decimal Number10355811.4%
 
Dash Punctuation52040.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
13900637.7%
 
01429413.8%
 
591448.8%
 
488078.5%
 
681717.9%
 
762696.1%
 
950264.9%
 
346814.5%
 
240903.9%
 
840703.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
121162100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L2131619.7%
 
C2037318.9%
 
A1575514.6%
 
B99709.2%
 
E71696.6%
 
R61645.7%
 
S44974.2%
 
V36243.4%
 
H36243.4%
 
P32063.0%
 
M31192.9%
 
G31012.9%
 
D22762.1%
 
O22762.1%
 
J15621.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a11821620.7%
 
e7531513.2%
 
l5727210.0%
 
r466958.2%
 
i427067.5%
 
n383666.7%
 
o358356.3%
 
s307785.4%
 
d299265.2%
 
u272364.8%
 
t166682.9%
 
b141872.5%
 
c78881.4%
 
z77311.4%
 
j69771.2%
 
m69761.2%
 
q31190.5%
 
y31010.5%
 
v16670.3%
 
p9140.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-5204100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin67960574.7%
 
Common22992425.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
12116252.7%
 
13900617.0%
 
0142946.2%
 
591444.0%
 
488073.8%
 
681713.6%
 
762692.7%
 
-52042.3%
 
950262.2%
 
346812.0%
 
240901.8%
 
840701.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a11821617.4%
 
e7531511.1%
 
l572728.4%
 
r466956.9%
 
i427066.3%
 
n383665.6%
 
o358355.3%
 
s307784.5%
 
d299264.4%
 
u272364.0%
 
L213163.1%
 
C203733.0%
 
t166682.5%
 
A157552.3%
 
b141872.1%
 
B99701.5%
 
c78881.2%
 
z77311.1%
 
E71691.1%
 
j69771.0%
 
m69761.0%
 
R61640.9%
 
S44970.7%
 
V36240.5%
 
H36240.5%
 
Other values (10)243413.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII909529100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
12116213.3%
 
a11821613.0%
 
e753158.3%
 
l572726.3%
 
r466955.1%
 
i427064.7%
 
1390064.3%
 
n383664.2%
 
o358353.9%
 
s307783.4%
 
d299263.3%
 
u272363.0%
 
L213162.3%
 
C203732.2%
 
t166681.8%
 
A157551.7%
 
0142941.6%
 
b141871.6%
 
B99701.1%
 
591441.0%
 
488071.0%
 
681710.9%
 
c78880.9%
 
z77310.9%
 
E71690.8%
 
Other values (22)855439.4%
 

Plana X Hurto
Real number (ℝ)

HIGH CORRELATION

Distinct42237
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean695495.1897
Minimum-1
Maximum851396.1355
Zeros0
Zeros (%)0.0%
Memory size546.5 KiB
2021-02-08T18:29:22.994091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1831169.0112
median834125.6375
Q3835864.0282
95-th percentile837195.8312
Maximum851396.1355
Range851397.1355
Interquartile range (IQR)4695.016996

Descriptive statistics

Standard deviation310756.3971
Coefficient of variation (CV)0.4468131508
Kurtosis1.208753076
Mean695495.1897
Median Absolute Deviation (MAD)2098.38985
Skewness-1.791198548
Sum4.864015159e+10
Variance9.656953834e+10
MonotocityNot monotonic
2021-02-08T18:29:23.109715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-11163816.6%
 
834649.4964890.1%
 
834154.3721830.1%
 
832280.1025720.1%
 
833257.6951690.1%
 
834451.507560.1%
 
834909.9804510.1%
 
831906.6143470.1%
 
835677.0096450.1%
 
833565.3502390.1%
 
834136.8306360.1%
 
834278.8647350.1%
 
831226.5751350.1%
 
832672.067433< 0.1%
 
831479.813132< 0.1%
 
836339.753932< 0.1%
 
835106.494632< 0.1%
 
835913.530932< 0.1%
 
835876.278931< 0.1%
 
834505.323531< 0.1%
 
833360.401130< 0.1%
 
836429.790530< 0.1%
 
832452.148530< 0.1%
 
834075.512930< 0.1%
 
835325.576529< 0.1%
 
Other values (42212)5726981.9%
 
ValueCountFrequency (%) 
-11163816.6%
 
820677.78521< 0.1%
 
821093.01471< 0.1%
 
821312.59461< 0.1%
 
821347.98351< 0.1%
 
821361.52341< 0.1%
 
821393.32111< 0.1%
 
821432.3241< 0.1%
 
821432.89631< 0.1%
 
821439.43861< 0.1%
 
ValueCountFrequency (%) 
851396.13551< 0.1%
 
844363.06781< 0.1%
 
843340.81511< 0.1%
 
843160.09231< 0.1%
 
843053.42431< 0.1%
 
843023.17022< 0.1%
 
843007.3221< 0.1%
 
842926.16741< 0.1%
 
842814.32971< 0.1%
 
842809.5661< 0.1%
 

Plana Y Hurto
Real number (ℝ)

HIGH CORRELATION

Distinct42184
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean986926.0242
Minimum-1
Maximum1194181.437
Zeros0
Zeros (%)0.0%
Memory size546.5 KiB
2021-02-08T18:29:23.238960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11180626.342
median1183224.592
Q31185578.101
95-th percentile1188288.277
Maximum1194181.437
Range1194182.437
Interquartile range (IQR)4951.759051

Descriptive statistics

Standard deviation440968.6148
Coefficient of variation (CV)0.4468102006
Kurtosis1.208842652
Mean986926.0242
Median Absolute Deviation (MAD)2445.066066
Skewness-1.791248106
Sum6.902165843e+10
Variance1.944533193e+11
MonotocityNot monotonic
2021-02-08T18:29:23.362646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-11163816.6%
 
1184822.522890.1%
 
1179249.839830.1%
 
1185715.971720.1%
 
1186940.876690.1%
 
1186034.474560.1%
 
1185972.773510.1%
 
1184608.044470.1%
 
1183492.887450.1%
 
1179224.134390.1%
 
1179061.922360.1%
 
1183200.424350.1%
 
1184863.174350.1%
 
1186147.24333< 0.1%
 
1184372.38232< 0.1%
 
1181891.69132< 0.1%
 
1184388.26132< 0.1%
 
1184721.12432< 0.1%
 
1186017.40531< 0.1%
 
1184085.1831< 0.1%
 
1185116.79330< 0.1%
 
1182965.89130< 0.1%
 
1181084.60230< 0.1%
 
1184578.19630< 0.1%
 
1183910.75929< 0.1%
 
Other values (42159)5726981.9%
 
ValueCountFrequency (%) 
-11163816.6%
 
1172579.5991< 0.1%
 
1173971.4811< 0.1%
 
1174005.3761< 0.1%
 
1174017.8581< 0.1%
 
1174262.7651< 0.1%
 
1174301.4971< 0.1%
 
1174305.0121< 0.1%
 
1174361.6721< 0.1%
 
1174369.3981< 0.1%
 
ValueCountFrequency (%) 
1194181.4371< 0.1%
 
1193873.4981< 0.1%
 
1193617.7711< 0.1%
 
1193571.1061< 0.1%
 
1193541.5651< 0.1%
 
1193533.361< 0.1%
 
1193518.7931< 0.1%
 
1193501.8811< 0.1%
 
1193497.9711< 0.1%
 
1193485.0021< 0.1%
 

Geo X Hurto
Real number (ℝ)

HIGH CORRELATION

Distinct39445
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-63.16445944
Minimum-75.69772524
Maximum-1
Zeros0
Zeros (%)0.0%
Memory size546.5 KiB
2021-02-08T18:29:23.494607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-75.69772524
5-th percentile-75.60801608
Q1-75.58372703
median-75.56500668
Q3-75.55222106
95-th percentile-1
Maximum-1
Range74.69772524
Interquartile range (IQR)0.03150597

Descriptive statistics

Standard deviation27.77523332
Coefficient of variation (CV)-0.4397288216
Kurtosis1.209078315
Mean-63.16445944
Median Absolute Deviation (MAD)0.01599524
Skewness1.791379466
Sum-4417469.635
Variance771.4635858
MonotocityNot monotonic
2021-02-08T18:29:23.605515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-11163816.6%
 
-75.571506890.1%
 
-75.575978830.1%
 
-75.592913720.1%
 
-75.584081690.1%
 
-75.573295560.1%
 
-75.569153510.1%
 
-75.596287470.1%
 
-75.562223450.1%
 
-75.581299390.1%
 
-75.576136360.1%
 
-75.574854350.1%
 
-75.60243350.1%
 
-75.56539834< 0.1%
 
-75.59223133< 0.1%
 
-75.58937233< 0.1%
 
-75.57280933< 0.1%
 
-75.55623532< 0.1%
 
-75.56008632< 0.1%
 
-75.56042232< 0.1%
 
-75.56737732< 0.1%
 
-75.60014132< 0.1%
 
-75.55542130< 0.1%
 
-75.57669230< 0.1%
 
-75.58315230< 0.1%
 
Other values (39420)5725881.9%
 
ValueCountFrequency (%) 
-75.697725241< 0.1%
 
-75.693968571< 0.1%
 
-75.691986381< 0.1%
 
-75.691699661< 0.1%
 
-75.691545431< 0.1%
 
-75.691291< 0.1%
 
-75.690905831< 0.1%
 
-75.690900471< 0.1%
 
-75.690841461< 0.1%
 
-75.690745161< 0.1%
 
ValueCountFrequency (%) 
-11163816.6%
 
-75.420205191< 0.1%
 
-75.483721< 0.1%
 
-75.492952391< 0.1%
 
-75.494591861< 0.1%
 
-75.495551821< 0.1%
 
-75.495827152< 0.1%
 
-75.495970551< 0.1%
 
-75.49670551< 0.1%
 
-75.497715421< 0.1%
 

Geo Y Hurto
Real number (ℝ)

HIGH CORRELATION

Distinct40034
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.049752013
Minimum-1
Maximum6.3500137
Zeros0
Zeros (%)0.0%
Memory size546.5 KiB
2021-02-08T18:29:23.857257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q16.227481415
median6.250943395
Q36.27220424
95-th percentile6.296718025
Maximum6.3500137
Range7.3500137
Interquartile range (IQR)0.044722825

Descriptive statistics

Standard deviation2.703140736
Coefficient of variation (CV)0.53530168
Kurtosis1.208561692
Mean5.049752013
Median Absolute Deviation (MAD)0.022103065
Skewness-1.791091584
Sum353159.4568
Variance7.306969838
MonotocityNot monotonic
2021-02-08T18:29:23.968833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-11163816.6%
 
6.265365890.1%
 
6.214985830.1%
 
6.273442720.1%
 
6.284516690.1%
 
6.276322560.1%
 
6.275764520.1%
 
6.263426470.1%
 
6.253345460.1%
 
6.214753390.1%
 
6.265733360.1%
 
6.213286360.1%
 
6.2507360.1%
 
6.23886833< 0.1%
 
6.27734133< 0.1%
 
6.26143932< 0.1%
 
6.26129632< 0.1%
 
6.26444932< 0.1%
 
6.27616831< 0.1%
 
6.25869931< 0.1%
 
6.26315730< 0.1%
 
6.26802630< 0.1%
 
6.25614230< 0.1%
 
6.23157230< 0.1%
 
6.2485830< 0.1%
 
Other values (40009)5726381.9%
 
ValueCountFrequency (%) 
-11163816.6%
 
6.154720211< 0.1%
 
6.167316921< 0.1%
 
6.167623291< 0.1%
 
6.16773611< 0.1%
 
6.1698941< 0.1%
 
6.170300181< 0.1%
 
6.170331851< 0.1%
 
6.170843671< 0.1%
 
6.170914051< 0.1%
 
ValueCountFrequency (%) 
6.35001371< 0.1%
 
6.347246761< 0.1%
 
6.3448791< 0.1%
 
6.344497751< 0.1%
 
6.344174451< 0.1%
 
6.344156531< 0.1%
 
6.3439691< 0.1%
 
6.343871771< 0.1%
 
6.343836631< 0.1%
 
6.343719341< 0.1%
 

Cuadrante
Categorical

HIGH CARDINALITY

Distinct413
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
SIN DATO
11697 
MEVALPNVCCD03E02000007
 
932
MEVALPNVCCD04E01000001
 
688
MEVALPNVCCD01E01000004
 
674
MEVALPNVCCD03E03000009
 
625
Other values (408)
55320 
ValueCountFrequency (%) 
SIN DATO1169716.7%
 
MEVALPNVCCD03E020000079321.3%
 
MEVALPNVCCD04E010000016881.0%
 
MEVALPNVCCD01E010000046741.0%
 
MEVALPNVCCD03E030000096250.9%
 
MEVALPNVCCD03E010000185740.8%
 
MEVALPNVCCD03E030000185380.8%
 
MEVALPNVCCD05E020000214820.7%
 
MEVALPNVCCD03E010000174690.7%
 
MEVALPNVCCD04E010000104670.7%
 
MEVALPNVCCD01E010000094510.6%
 
MEVALPNVCCD03E020000084390.6%
 
MEVALPNVCCD05E020000204340.6%
 
MEVALPNVCCD03E030000024330.6%
 
MEVALPNVCCD02E010000184320.6%
 
MEVALPNVCCD03E030000034270.6%
 
MEVALPNVCCD05E020000044250.6%
 
MEVALPNVCCD05E020000014170.6%
 
MEVALPNVCCD02E010000404150.6%
 
MEVALPNVCCD04E010000074120.6%
 
MEVALPNVCCD03E010000564090.6%
 
MEVALPNVCCD02E010000333990.6%
 
MEVALPNVCCD02E010000343900.6%
 
MEVALPNVCCD03E010000193850.6%
 
MEVALPNVCCD02E010000043760.5%
 
Other values (388)4654666.6%
 
2021-02-08T18:29:24.106222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2021-02-08T18:29:24.224765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length19.65845916
Min length8

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
037506127.3%
 
E1164788.5%
 
V1164788.5%
 
C1164788.5%
 
A699365.1%
 
N699365.1%
 
D699365.1%
 
1644124.7%
 
M582394.2%
 
L582394.2%
 
P582394.2%
 
2427893.1%
 
3378582.8%
 
4235261.7%
 
5152021.1%
 
S116970.9%
 
I116970.9%
 
116970.9%
 
T116970.9%
 
O116970.9%
 
762270.5%
 
661040.4%
 
856600.4%
 
955510.4%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter78074756.8%
 
Decimal Number58239042.4%
 
Space Separator116970.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E11647814.9%
 
V11647814.9%
 
C11647814.9%
 
A699369.0%
 
N699369.0%
 
D699369.0%
 
M582397.5%
 
L582397.5%
 
P582397.5%
 
S116971.5%
 
I116971.5%
 
T116971.5%
 
O116971.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
037506164.4%
 
16441211.1%
 
2427897.3%
 
3378586.5%
 
4235264.0%
 
5152022.6%
 
762271.1%
 
661041.0%
 
856601.0%
 
955511.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
11697100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin78074756.8%
 
Common59408743.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E11647814.9%
 
V11647814.9%
 
C11647814.9%
 
A699369.0%
 
N699369.0%
 
D699369.0%
 
M582397.5%
 
L582397.5%
 
P582397.5%
 
S116971.5%
 
I116971.5%
 
T116971.5%
 
O116971.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
037506163.1%
 
16441210.8%
 
2427897.2%
 
3378586.4%
 
4235264.0%
 
5152022.6%
 
116972.0%
 
762271.0%
 
661041.0%
 
856601.0%
 
955510.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1374834100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
037506127.3%
 
E1164788.5%
 
V1164788.5%
 
C1164788.5%
 
A699365.1%
 
N699365.1%
 
D699365.1%
 
1644124.7%
 
M582394.2%
 
L582394.2%
 
P582394.2%
 
2427893.1%
 
3378582.8%
 
4235261.7%
 
5152021.1%
 
S116970.9%
 
I116970.9%
 
116970.9%
 
T116970.9%
 
O116970.9%
 
762270.5%
 
661040.4%
 
856600.4%
 
955510.4%
 

Lugar
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Vía pública
48362 
Residencia
11078 
Vehículo particular
 
2163
Parqueadero
 
2030
Sin dato
 
999
Other values (72)
5304 
ValueCountFrequency (%) 
Vía pública4836269.2%
 
Residencia1107815.8%
 
Vehículo particular21633.1%
 
Parqueadero20302.9%
 
Sin dato9991.4%
 
Edificio7011.0%
 
Fábrica o empresa6661.0%
 
Almacén tienda y otro4020.6%
 
Hotel, motel y hostal3850.6%
 
Conjunto residencial3640.5%
 
Hospital o centro de salud3540.5%
 
Institución educativa (jardín, primaria o secundaria)2910.4%
 
Centro comercial2530.4%
 
Casa o apartamento2500.4%
 
Parque2310.3%
 
Oficina1510.2%
 
Iglesia1360.2%
 
Bar o cantina1360.2%
 
Institución de educación superior1120.2%
 
Escenario deportivo1000.1%
 
Taller mecánico930.1%
 
Restaurante820.1%
 
Banco800.1%
 
Plaza de mercado510.1%
 
Local comercial480.1%
 
Other values (52)4180.6%
 
2021-02-08T18:29:24.341850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)< 0.1%
2021-02-08T18:29:24.456149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length53
Median length11
Mean length11.58027339
Min length4

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a12682715.7%
 
i8170610.1%
 
c690798.5%
 
607157.5%
 
l563057.0%
 
p524206.5%
 
í508616.3%
 
V505256.2%
 
b491366.1%
 
ú483816.0%
 
e357574.4%
 
d178742.2%
 
n175482.2%
 
s148951.8%
 
r143361.8%
 
o129411.6%
 
R111611.4%
 
t88691.1%
 
u87331.1%
 
m25700.3%
 
h25480.3%
 
P23560.3%
 
q22610.3%
 
S10140.1%
 
C9200.1%
 
Other values (26)101401.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter67792283.7%
 
Uppercase Letter699758.6%
 
Space Separator607157.5%
 
Other Punctuation6840.1%
 
Open Punctuation291< 0.1%
 
Close Punctuation291< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V5052572.2%
 
R1116115.9%
 
P23563.4%
 
S10141.4%
 
C9201.3%
 
E8861.3%
 
H7431.1%
 
F6931.0%
 
I5640.8%
 
A4090.6%
 
B2570.4%
 
O1610.2%
 
T1430.2%
 
L660.1%
 
M470.1%
 
Q11< 0.1%
 
D8< 0.1%
 
G7< 0.1%
 
Z4< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a12682718.7%
 
i8170612.1%
 
c6907910.2%
 
l563058.3%
 
p524207.7%
 
í508617.5%
 
b491367.2%
 
ú483817.1%
 
e357575.3%
 
d178742.6%
 
n175482.6%
 
s148952.2%
 
r143362.1%
 
o129411.9%
 
t88691.3%
 
u87331.3%
 
m25700.4%
 
h25480.4%
 
q22610.3%
 
f8810.1%
 
y7940.1%
 
á7590.1%
 
j6710.1%
 
ó6300.1%
 
v4220.1%
 
Other values (3)7180.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
60715100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(291100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,684100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)291100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin74789792.3%
 
Common619817.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a12682717.0%
 
i8170610.9%
 
c690799.2%
 
l563057.5%
 
p524207.0%
 
í508616.8%
 
V505256.8%
 
b491366.6%
 
ú483816.5%
 
e357574.8%
 
d178742.4%
 
n175482.3%
 
s148952.0%
 
r143361.9%
 
o129411.7%
 
R111611.5%
 
t88691.2%
 
u87331.2%
 
m25700.3%
 
h25480.3%
 
P23560.3%
 
q22610.3%
 
S10140.1%
 
C9200.1%
 
E8860.1%
 
Other values (22)79881.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
6071598.0%
 
,6841.1%
 
(2910.5%
 
)2910.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII70883887.5%
 
None10104012.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a12682717.9%
 
i8170611.5%
 
c690799.7%
 
607158.6%
 
l563057.9%
 
p524207.4%
 
V505257.1%
 
b491366.9%
 
e357575.0%
 
d178742.5%
 
n175482.5%
 
s148952.1%
 
r143362.0%
 
o129411.8%
 
R111611.6%
 
t88691.3%
 
u87331.2%
 
m25700.4%
 
h25480.4%
 
P23560.3%
 
q22610.3%
 
S10140.1%
 
C9200.1%
 
E8860.1%
 
f8810.1%
 
Other values (21)65750.9%
 

Most frequent None characters

ValueCountFrequency (%) 
í5086150.3%
 
ú4838147.9%
 
á7590.8%
 
ó6300.6%
 
é4090.4%
 

Grupo Lugar
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Transporte
50672 
Habitacional
12100 
Económica
 
4699
Sin dato
 
999
Social
 
757
Other values (6)
 
709
ValueCountFrequency (%) 
Transporte5067272.5%
 
Habitacional1210017.3%
 
Económica46996.7%
 
Sin dato9991.4%
 
Social7571.1%
 
Cultura deportes y recreación2640.4%
 
Otro2590.4%
 
Religioso1360.2%
 
Gubernamental25< 0.1%
 
Topográfico20< 0.1%
 
Ilegal5< 0.1%
 
2021-02-08T18:29:24.559421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:24.655979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length10
Mean length10.25560512
Min length4

Overview of Unicode Properties

Unique unicode characters30
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r10270414.3%
 
a9401013.1%
 
o700829.8%
 
n687849.6%
 
t645839.0%
 
e519197.2%
 
s510727.1%
 
p509567.1%
 
T506927.1%
 
i312114.4%
 
c228033.2%
 
l132921.9%
 
b121251.7%
 
H121001.7%
 
ó49630.7%
 
m47240.7%
 
E46990.7%
 
17910.2%
 
S17560.2%
 
d12630.2%
 
u5530.1%
 
C264< 0.1%
 
y264< 0.1%
 
O259< 0.1%
 
g161< 0.1%
 
Other values (5)206< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter64550990.0%
 
Uppercase Letter699369.8%
 
Space Separator17910.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T5069272.5%
 
H1210017.3%
 
E46996.7%
 
S17562.5%
 
C2640.4%
 
O2590.4%
 
R1360.2%
 
G25< 0.1%
 
I5< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r10270415.9%
 
a9401014.6%
 
o7008210.9%
 
n6878410.7%
 
t6458310.0%
 
e519198.0%
 
s510727.9%
 
p509567.9%
 
i312114.8%
 
c228033.5%
 
l132922.1%
 
b121251.9%
 
ó49630.8%
 
m47240.7%
 
d12630.2%
 
u5530.1%
 
y264< 0.1%
 
g161< 0.1%
 
á20< 0.1%
 
f20< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1791100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin71544599.8%
 
Common17910.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r10270414.4%
 
a9401013.1%
 
o700829.8%
 
n687849.6%
 
t645839.0%
 
e519197.3%
 
s510727.1%
 
p509567.1%
 
T506927.1%
 
i312114.4%
 
c228033.2%
 
l132921.9%
 
b121251.7%
 
H121001.7%
 
ó49630.7%
 
m47240.7%
 
E46990.7%
 
S17560.2%
 
d12630.2%
 
u5530.1%
 
C264< 0.1%
 
y264< 0.1%
 
O259< 0.1%
 
g161< 0.1%
 
R136< 0.1%
 
Other values (4)70< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
1791100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII71225399.3%
 
None49830.7%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r10270414.4%
 
a9401013.2%
 
o700829.8%
 
n687849.7%
 
t645839.1%
 
e519197.3%
 
s510727.2%
 
p509567.2%
 
T506927.1%
 
i312114.4%
 
c228033.2%
 
l132921.9%
 
b121251.7%
 
H121001.7%
 
m47240.7%
 
E46990.7%
 
17910.3%
 
S17560.2%
 
d12630.2%
 
u5530.1%
 
C264< 0.1%
 
y264< 0.1%
 
O259< 0.1%
 
g161< 0.1%
 
R136< 0.1%
 
Other values (3)50< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ó496399.6%
 
á200.4%
 

Valor Hurto
Real number (ℝ≥0)

SKEWED

Distinct1282
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4449911.33
Minimum0
Maximum8100000000
Zeros95
Zeros (%)0.1%
Memory size546.5 KiB
2021-02-08T18:29:24.759058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1100000
Q12300000
median3390000
Q34700000
95-th percentile8000000
Maximum8100000000
Range8100000000
Interquartile range (IQR)2400000

Descriptive statistics

Standard deviation46800472.87
Coefficient of variation (CV)10.51716976
Kurtosis19995.40639
Mean4449911.33
Median Absolute Deviation (MAD)1110000
Skewness135.0868956
Sum3.112089988e+11
Variance2.190284261e+15
MonotocityNot monotonic
2021-02-08T18:29:24.881305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
300000055527.9%
 
250000045136.5%
 
350000044316.3%
 
200000043776.3%
 
400000041716.0%
 
500000030144.3%
 
450000024213.5%
 
150000024173.5%
 
100000022263.2%
 
600000018842.7%
 
180000016462.4%
 
320000015432.2%
 
280000014222.0%
 
700000012131.7%
 
380000011691.7%
 
550000011011.6%
 
12000009201.3%
 
22000008521.2%
 
36000007671.1%
 
23000007451.1%
 
27000007341.0%
 
26000007341.0%
 
42000007161.0%
 
16000006901.0%
 
33000006721.0%
 
Other values (1257)2000628.6%
 
ValueCountFrequency (%) 
0950.1%
 
1530.1%
 
51< 0.1%
 
20072< 0.1%
 
20091< 0.1%
 
20101< 0.1%
 
20113< 0.1%
 
20121< 0.1%
 
20131< 0.1%
 
20141< 0.1%
 
ValueCountFrequency (%) 
81000000001< 0.1%
 
65000000001< 0.1%
 
43700000001< 0.1%
 
36000000001< 0.1%
 
31750000001< 0.1%
 
10358745961< 0.1%
 
10008977831< 0.1%
 
3750000001< 0.1%
 
2700001001< 0.1%
 
1800000001< 0.1%
 

Modelo Hurto
Real number (ℝ)

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1486.85737
Minimum-1
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size546.5 KiB
2021-02-08T18:29:25.011428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2004
Q32011
95-th percentile2015
Maximum2015
Range2016
Interquartile range (IQR)2012

Descriptive statistics

Standard deviation878.2808299
Coefficient of variation (CV)0.5906960868
Kurtosis-0.7816432557
Mean1486.85737
Median Absolute Deviation (MAD)9
Skewness-1.103639469
Sum103984857
Variance771377.2162
MonotocityNot monotonic
2021-02-08T18:29:25.135539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-11807125.8%
 
201443506.2%
 
201537585.4%
 
200634725.0%
 
201333854.8%
 
201233154.7%
 
200829954.3%
 
200728644.1%
 
201127093.9%
 
200524273.5%
 
201022203.2%
 
200920392.9%
 
199819672.8%
 
199515622.2%
 
200415492.2%
 
199615052.2%
 
199713792.0%
 
200313281.9%
 
199911781.7%
 
200010511.5%
 
200210051.4%
 
19949551.4%
 
20018731.2%
 
19936090.9%
 
19784600.7%
 
Other values (25)29104.2%
 
ValueCountFrequency (%) 
-11807125.8%
 
19611< 0.1%
 
19641< 0.1%
 
19663< 0.1%
 
19673< 0.1%
 
19709< 0.1%
 
19722< 0.1%
 
197322< 0.1%
 
19746< 0.1%
 
19758< 0.1%
 
ValueCountFrequency (%) 
201537585.4%
 
201443506.2%
 
201333854.8%
 
201233154.7%
 
201127093.9%
 
201022203.2%
 
200920392.9%
 
200829954.3%
 
200728644.1%
 
200634725.0%
 

Linea Marca Hurto
Categorical

HIGH CARDINALITY

Distinct1042
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
LINEA STANDARD
6143 
SIN DATO
5021 
AK 125
 
3476
BWS
 
3297
BOXER
 
3281
Other values (1037)
48718 
ValueCountFrequency (%) 
LINEA STANDARD61438.8%
 
SIN DATO50217.2%
 
AK 12534765.0%
 
BWS32974.7%
 
BOXER32814.7%
 
BOXER CT 10031364.5%
 
PULSAR24113.4%
 
RX 11518132.6%
 
DT12517822.5%
 
AK125 NKDR12461.8%
 
8012451.8%
 
AX 10010861.6%
 
AK 11010811.5%
 
YBR12510291.5%
 
ECO9991.4%
 
AGILITY RS9721.4%
 
RX 1009021.3%
 
CRIPTON 1108891.3%
 
YBR-125ED7631.1%
 
AK 125 NKD7511.1%
 
1007441.1%
 
100 CC7031.0%
 
1256771.0%
 
YW125X - BWS 125X6691.0%
 
LIBERO6420.9%
 
Other values (1017)2517836.0%
 
2021-02-08T18:29:25.275996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique361 ?
Unique (%)0.5%
2021-02-08T18:29:25.400160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length6
Mean length7.894289064
Min length1

Overview of Unicode Properties

Unique unicode characters45
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
532739.6%
 
A444918.1%
 
1430567.8%
 
R344206.2%
 
0299155.4%
 
S281445.1%
 
D270184.9%
 
T263904.8%
 
5248634.5%
 
N239554.3%
 
E225294.1%
 
I222174.0%
 
O201983.7%
 
2201913.7%
 
B167253.0%
 
X166493.0%
 
L164833.0%
 
C129862.4%
 
K120332.2%
 
Y78161.4%
 
P76381.4%
 
W69161.3%
 
U59351.1%
 
G52561.0%
 
-41050.7%
 
Other values (20)188933.4%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter36794266.6%
 
Decimal Number12511722.7%
 
Space Separator532739.6%
 
Dash Punctuation41050.7%
 
Open Punctuation5760.1%
 
Close Punctuation5760.1%
 
Other Punctuation4460.1%
 
Math Symbol60< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A4449112.1%
 
R344209.4%
 
S281447.6%
 
D270187.3%
 
T263907.2%
 
N239556.5%
 
E225296.1%
 
I222176.0%
 
O201985.5%
 
B167254.5%
 
X166494.5%
 
L164834.5%
 
C129863.5%
 
K120333.3%
 
Y78162.1%
 
P76382.1%
 
W69161.9%
 
U59351.6%
 
G52561.4%
 
V29440.8%
 
F25500.7%
 
Z24870.7%
 
M17300.5%
 
J2090.1%
 
H182< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
53273100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14305634.4%
 
02991523.9%
 
52486319.9%
 
22019116.1%
 
832302.6%
 
616421.3%
 
98360.7%
 
77520.6%
 
35480.4%
 
4840.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4105100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.41492.8%
 
/265.8%
 
&30.7%
 
,30.7%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+60100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(576100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)576100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin36794266.6%
 
Common18415333.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A4449112.1%
 
R344209.4%
 
S281447.6%
 
D270187.3%
 
T263907.2%
 
N239556.5%
 
E225296.1%
 
I222176.0%
 
O201985.5%
 
B167254.5%
 
X166494.5%
 
L164834.5%
 
C129863.5%
 
K120333.3%
 
Y78162.1%
 
P76382.1%
 
W69161.9%
 
U59351.6%
 
G52561.4%
 
V29440.8%
 
F25500.7%
 
Z24870.7%
 
M17300.5%
 
J2090.1%
 
H182< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
5327328.9%
 
14305623.4%
 
02991516.2%
 
52486313.5%
 
22019111.0%
 
-41052.2%
 
832301.8%
 
616420.9%
 
98360.5%
 
77520.4%
 
(5760.3%
 
)5760.3%
 
35480.3%
 
.4140.2%
 
484< 0.1%
 
+60< 0.1%
 
/26< 0.1%
 
&3< 0.1%
 
,3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII552095100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
532739.6%
 
A444918.1%
 
1430567.8%
 
R344206.2%
 
0299155.4%
 
S281445.1%
 
D270184.9%
 
T263904.8%
 
5248634.5%
 
N239554.3%
 
E225294.1%
 
I222174.0%
 
O201983.7%
 
2201913.7%
 
B167253.0%
 
X166493.0%
 
L164833.0%
 
C129862.4%
 
K120332.2%
 
Y78161.4%
 
P76381.4%
 
W69161.3%
 
U59351.1%
 
G52561.0%
 
-41050.7%
 
Other values (20)188933.4%
 

Medio Transporte
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Caminata
28404 
Motocicleta
28399 
Sin dato
12794 
Automóvil
 
168
Motocicleta con parrillero
 
103
Other values (3)
 
68
ValueCountFrequency (%) 
Caminata2840440.6%
 
Motocicleta2839940.6%
 
Sin dato1279418.3%
 
Automóvil1680.2%
 
Motocicleta con parrillero1030.1%
 
Taxi430.1%
 
Autobus16< 0.1%
 
Bicicleta9< 0.1%
 
2021-02-08T18:29:25.511131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:25.689048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:25.797309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length8
Mean length9.244566461
Min length4

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a12666319.6%
 
t9839515.2%
 
o7018810.9%
 
i7003210.8%
 
c571258.8%
 
n413016.4%
 
l288854.5%
 
e286144.4%
 
m285724.4%
 
M285024.4%
 
C284044.4%
 
130002.0%
 
S127942.0%
 
d127942.0%
 
r309< 0.1%
 
u200< 0.1%
 
A184< 0.1%
 
ó168< 0.1%
 
v168< 0.1%
 
p103< 0.1%
 
T43< 0.1%
 
x43< 0.1%
 
b16< 0.1%
 
s16< 0.1%
 
B9< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter56359287.2%
 
Uppercase Letter6993610.8%
 
Space Separator130002.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M2850240.8%
 
C2840440.6%
 
S1279418.3%
 
A1840.3%
 
T430.1%
 
B9< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a12666322.5%
 
t9839517.5%
 
o7018812.5%
 
i7003212.4%
 
c5712510.1%
 
n413017.3%
 
l288855.1%
 
e286145.1%
 
m285725.1%
 
d127942.3%
 
r3090.1%
 
u200< 0.1%
 
ó168< 0.1%
 
v168< 0.1%
 
p103< 0.1%
 
x43< 0.1%
 
b16< 0.1%
 
s16< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
13000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin63352898.0%
 
Common130002.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a12666320.0%
 
t9839515.5%
 
o7018811.1%
 
i7003211.1%
 
c571259.0%
 
n413016.5%
 
l288854.6%
 
e286144.5%
 
m285724.5%
 
M285024.5%
 
C284044.5%
 
S127942.0%
 
d127942.0%
 
r309< 0.1%
 
u200< 0.1%
 
A184< 0.1%
 
ó168< 0.1%
 
v168< 0.1%
 
p103< 0.1%
 
T43< 0.1%
 
x43< 0.1%
 
b16< 0.1%
 
s16< 0.1%
 
B9< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
13000100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII646360> 99.9%
 
None168< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a12666319.6%
 
t9839515.2%
 
o7018810.9%
 
i7003210.8%
 
c571258.8%
 
n413016.4%
 
l288854.5%
 
e286144.4%
 
m285724.4%
 
M285024.4%
 
C284044.4%
 
130002.0%
 
S127942.0%
 
d127942.0%
 
r309< 0.1%
 
u200< 0.1%
 
A184< 0.1%
 
v168< 0.1%
 
p103< 0.1%
 
T43< 0.1%
 
x43< 0.1%
 
b16< 0.1%
 
s16< 0.1%
 
B9< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ó168100.0%
 

Medio Transporte Agresor
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Caminata
34001 
Sin dato
15016 
Motocicleta
12695 
Motocicleta con parrillero
7687 
Automóvil
 
339
Other values (4)
 
198
ValueCountFrequency (%) 
Caminata3400148.6%
 
Sin dato1501621.5%
 
Motocicleta1269518.2%
 
Motocicleta con parrillero768711.0%
 
Automóvil3390.5%
 
Taxi1710.2%
 
Bicicleta18< 0.1%
 
Metro5< 0.1%
 
Autobus4< 0.1%
 
2021-02-08T18:29:25.891684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:25.959859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:26.070506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length8
Mean length10.51808797
Min length4

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a14527719.7%
 
t9014712.3%
 
i7763210.6%
 
o715029.7%
 
n567047.7%
 
c484876.6%
 
l361134.9%
 
m343404.7%
 
C340014.6%
 
303904.1%
 
e280923.8%
 
r230663.1%
 
M203872.8%
 
S150162.0%
 
d150162.0%
 
p76871.0%
 
u347< 0.1%
 
A343< 0.1%
 
ó339< 0.1%
 
v339< 0.1%
 
T171< 0.1%
 
x171< 0.1%
 
B18< 0.1%
 
b4< 0.1%
 
s4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter63526786.4%
 
Uppercase Letter699369.5%
 
Space Separator303904.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C3400148.6%
 
M2038729.2%
 
S1501621.5%
 
A3430.5%
 
T1710.2%
 
B18< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a14527722.9%
 
t9014714.2%
 
i7763212.2%
 
o7150211.3%
 
n567048.9%
 
c484877.6%
 
l361135.7%
 
m343405.4%
 
e280924.4%
 
r230663.6%
 
d150162.4%
 
p76871.2%
 
u3470.1%
 
ó3390.1%
 
v3390.1%
 
x171< 0.1%
 
b4< 0.1%
 
s4< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
30390100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin70520395.9%
 
Common303904.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a14527720.6%
 
t9014712.8%
 
i7763211.0%
 
o7150210.1%
 
n567048.0%
 
c484876.9%
 
l361135.1%
 
m343404.9%
 
C340014.8%
 
e280924.0%
 
r230663.3%
 
M203872.9%
 
S150162.1%
 
d150162.1%
 
p76871.1%
 
u347< 0.1%
 
A343< 0.1%
 
ó339< 0.1%
 
v339< 0.1%
 
T171< 0.1%
 
x171< 0.1%
 
B18< 0.1%
 
b4< 0.1%
 
s4< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
30390100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII735254> 99.9%
 
None339< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a14527719.8%
 
t9014712.3%
 
i7763210.6%
 
o715029.7%
 
n567047.7%
 
c484876.6%
 
l361134.9%
 
m343404.7%
 
C340014.6%
 
303904.1%
 
e280923.8%
 
r230663.1%
 
M203872.8%
 
S150162.0%
 
d150162.0%
 
p76871.0%
 
u347< 0.1%
 
A343< 0.1%
 
v339< 0.1%
 
T171< 0.1%
 
x171< 0.1%
 
B18< 0.1%
 
b4< 0.1%
 
s4< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
ó339100.0%
 

Sede Receptora
Categorical

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Candelaria
12299 
Castilla
11769 
Belén
7858 
Laureles
7664 
Aranjuez
6532 
Other values (17)
23814 
ValueCountFrequency (%) 
Candelaria1229917.6%
 
Castilla1176916.8%
 
Belén785811.2%
 
Laureles766411.0%
 
Aranjuez65329.3%
 
Villa Hermosa57008.2%
 
Sin dato38535.5%
 
Manrique31844.6%
 
Doce de Octubre31034.4%
 
Buenos Aires25853.7%
 
Poblado17332.5%
 
San Javier15882.3%
 
Popular8601.2%
 
Santa Cruz7381.1%
 
San Antonio de Prado3360.5%
 
Itagüí540.1%
 
Bello440.1%
 
Envigado15< 0.1%
 
Terminal del norte12< 0.1%
 
Sabaneta4< 0.1%
 
Copacabana3< 0.1%
 
Área investigativa delitos contra el patrimonio económico2< 0.1%
 
2021-02-08T18:29:26.171942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:26.271319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length57
Median length8
Mean length8.96745596
Min length5

Overview of Unicode Properties

Unique unicode characters41
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a10110016.1%
 
e6739810.7%
 
l6546810.4%
 
r446197.1%
 
i413556.6%
 
n396996.3%
 
s303074.8%
 
C248094.0%
 
u246663.9%
 
217143.5%
 
d216893.5%
 
o206613.3%
 
t198793.2%
 
B104871.7%
 
A94531.5%
 
é78581.3%
 
L76641.2%
 
z72701.2%
 
j65321.0%
 
S65191.0%
 
c62151.0%
 
m57160.9%
 
V57000.9%
 
H57000.9%
 
b48430.8%
 
Other values (16)198273.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter52111283.1%
 
Uppercase Letter8432213.4%
 
Space Separator217143.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C2480929.4%
 
B1048712.4%
 
A945311.2%
 
L76649.1%
 
S65197.7%
 
V57006.8%
 
H57006.8%
 
M31843.8%
 
D31033.7%
 
O31033.7%
 
P29293.5%
 
J15881.9%
 
I540.1%
 
E15< 0.1%
 
T12< 0.1%
 
Á2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a10110019.4%
 
e6739812.9%
 
l6546812.6%
 
r446198.6%
 
i413557.9%
 
n396997.6%
 
s303075.8%
 
u246664.7%
 
d216894.2%
 
o206614.0%
 
t198793.8%
 
é78581.5%
 
z72701.4%
 
j65321.3%
 
c62151.2%
 
m57161.1%
 
b48430.9%
 
q31840.6%
 
v16070.3%
 
p8650.2%
 
g71< 0.1%
 
ü54< 0.1%
 
í54< 0.1%
 
ó2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
21714100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin60543496.5%
 
Common217143.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a10110016.7%
 
e6739811.1%
 
l6546810.8%
 
r446197.4%
 
i413556.8%
 
n396996.6%
 
s303075.0%
 
C248094.1%
 
u246664.1%
 
d216893.6%
 
o206613.4%
 
t198793.3%
 
B104871.7%
 
A94531.6%
 
é78581.3%
 
L76641.3%
 
z72701.2%
 
j65321.1%
 
S65191.1%
 
c62151.0%
 
m57160.9%
 
V57000.9%
 
H57000.9%
 
b48430.8%
 
M31840.5%
 
Other values (15)166432.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
21714100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII61917898.7%
 
None79701.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a10110016.3%
 
e6739810.9%
 
l6546810.6%
 
r446197.2%
 
i413556.7%
 
n396996.4%
 
s303074.9%
 
C248094.0%
 
u246664.0%
 
217143.5%
 
d216893.5%
 
o206613.3%
 
t198793.2%
 
B104871.7%
 
A94531.5%
 
L76641.2%
 
z72701.2%
 
j65321.1%
 
S65191.1%
 
c62151.0%
 
m57160.9%
 
V57000.9%
 
H57000.9%
 
b48430.8%
 
M31840.5%
 
Other values (11)165312.7%
 

Most frequent None characters

ValueCountFrequency (%) 
é785898.6%
 
ü540.7%
 
í540.7%
 
Á2< 0.1%
 
ó2< 0.1%
 

Marca
Categorical

HIGH CARDINALITY

Distinct79
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Yamaha
25112 
Bajaj
10472 
AKT
9075 
Auteco
6439 
Suzuki
5506 
Other values (74)
13332 
ValueCountFrequency (%) 
Yamaha2511235.9%
 
Bajaj1047215.0%
 
AKT907513.0%
 
Auteco64399.2%
 
Suzuki55067.9%
 
Honda46286.6%
 
Kawasaki33344.8%
 
KYMCO27974.0%
 
Sin dato12711.8%
 
Piaggio2080.3%
 
Otra1690.2%
 
KTM1590.2%
 
Hero1530.2%
 
Jialing770.1%
 
BMW660.1%
 
United Motors620.1%
 
QINGQI580.1%
 
Jincheng530.1%
 
Chevrolet410.1%
 
Peugeot34< 0.1%
 
UM PowerMax30< 0.1%
 
Abarth23< 0.1%
 
SKY GO20< 0.1%
 
victory18< 0.1%
 
Ayco14< 0.1%
 
Other values (54)1170.2%
 
2021-02-08T18:29:26.381301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique21 ?
Unique (%)< 0.1%
2021-02-08T18:29:26.499409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length6
Mean length5.485300846
Min length3

Overview of Unicode Properties

Unique unicode characters53
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a11275929.4%
 
Y279327.3%
 
h252476.6%
 
m251386.6%
 
j209555.5%
 
u175144.6%
 
A155614.1%
 
K153904.0%
 
o130193.4%
 
i108802.8%
 
B105432.7%
 
T92372.4%
 
k88582.3%
 
t81592.1%
 
e69461.8%
 
S68131.8%
 
c65351.7%
 
n62161.6%
 
d59671.6%
 
z55171.4%
 
H47861.2%
 
s34180.9%
 
w33710.9%
 
M31550.8%
 
O29860.8%
 
Other values (28)67181.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter28199473.5%
 
Uppercase Letter10022726.1%
 
Space Separator13970.4%
 
Other Punctuation2< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y2793227.9%
 
A1556115.5%
 
K1539015.4%
 
B1054310.5%
 
T92379.2%
 
S68136.8%
 
H47864.8%
 
M31553.1%
 
O29863.0%
 
C28412.8%
 
P2730.3%
 
J1340.1%
 
I1180.1%
 
Q1160.1%
 
U930.1%
 
G800.1%
 
W720.1%
 
N600.1%
 
L11< 0.1%
 
D6< 0.1%
 
E6< 0.1%
 
R4< 0.1%
 
V4< 0.1%
 
Z4< 0.1%
 
F2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a11275940.0%
 
h252479.0%
 
m251388.9%
 
j209557.4%
 
u175146.2%
 
o130194.6%
 
i108803.9%
 
k88583.1%
 
t81592.9%
 
e69462.5%
 
c65352.3%
 
n62162.2%
 
d59672.1%
 
z55172.0%
 
s34181.2%
 
w33711.2%
 
g6220.2%
 
r5340.2%
 
l1450.1%
 
v63< 0.1%
 
y49< 0.1%
 
x36< 0.1%
 
b34< 0.1%
 
f10< 0.1%
 
p1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1397100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin38222199.6%
 
Common13990.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a11275929.5%
 
Y279327.3%
 
h252476.6%
 
m251386.6%
 
j209555.5%
 
u175144.6%
 
A155614.1%
 
K153904.0%
 
o130193.4%
 
i108802.8%
 
B105432.8%
 
T92372.4%
 
k88582.3%
 
t81592.1%
 
e69461.8%
 
S68131.8%
 
c65351.7%
 
n62161.6%
 
d59671.6%
 
z55171.4%
 
H47861.3%
 
s34180.9%
 
w33710.9%
 
M31550.8%
 
O29860.8%
 
Other values (26)53191.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
139799.9%
 
.20.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII383620100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a11275929.4%
 
Y279327.3%
 
h252476.6%
 
m251386.6%
 
j209555.5%
 
u175144.6%
 
A155614.1%
 
K153904.0%
 
o130193.4%
 
i108802.8%
 
B105432.7%
 
T92372.4%
 
k88582.3%
 
t81592.1%
 
e69461.8%
 
S68131.8%
 
c65351.7%
 
n62161.6%
 
d59671.6%
 
z55171.4%
 
H47861.2%
 
s34180.9%
 
w33710.9%
 
M31550.8%
 
O29860.8%
 
Other values (28)67181.8%
 

Color
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Negro
26822 
Azul
12009 
Blanco
9645 
Rojo
8787 
Gris
5019 
Other values (11)
7654 
ValueCountFrequency (%) 
Negro2682238.4%
 
Azul1200917.2%
 
Blanco964513.8%
 
Rojo878712.6%
 
Gris50197.2%
 
Verde22473.2%
 
Sin dato15452.2%
 
Amarillo8871.3%
 
Morado8751.3%
 
Naranja6410.9%
 
Café6400.9%
 
Plata5680.8%
 
Beige1240.2%
 
Oro910.1%
 
Rosado32< 0.1%
 
Bronce4< 0.1%
 
2021-02-08T18:29:26.598447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:26.691023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length5
Mean length4.892716198
Min length3

Overview of Unicode Properties

Unique unicode characters30
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o5838217.1%
 
r3658610.7%
 
e315689.2%
 
N274638.0%
 
g269467.9%
 
l239967.0%
 
a166834.9%
 
A128963.8%
 
z120093.5%
 
u120093.5%
 
n118353.5%
 
B97732.9%
 
c96492.8%
 
j94282.8%
 
R88192.6%
 
i75752.2%
 
s50511.5%
 
G50191.5%
 
d46991.4%
 
V22470.7%
 
t21130.6%
 
S15450.5%
 
15450.5%
 
m8870.3%
 
M8750.3%
 
Other values (5)25790.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter27069679.1%
 
Uppercase Letter6993620.4%
 
Space Separator15450.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N2746339.3%
 
A1289618.4%
 
B977314.0%
 
R881912.6%
 
G50197.2%
 
V22473.2%
 
S15452.2%
 
M8751.3%
 
C6400.9%
 
P5680.8%
 
O910.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5838221.6%
 
r3658613.5%
 
e3156811.7%
 
g2694610.0%
 
l239968.9%
 
a166836.2%
 
z120094.4%
 
u120094.4%
 
n118354.4%
 
c96493.6%
 
j94283.5%
 
i75752.8%
 
s50511.9%
 
d46991.7%
 
t21130.8%
 
m8870.3%
 
f6400.2%
 
é6400.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1545100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin34063299.5%
 
Common15450.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o5838217.1%
 
r3658610.7%
 
e315689.3%
 
N274638.1%
 
g269467.9%
 
l239967.0%
 
a166834.9%
 
A128963.8%
 
z120093.5%
 
u120093.5%
 
n118353.5%
 
B97732.9%
 
c96492.8%
 
j94282.8%
 
R88192.6%
 
i75752.2%
 
s50511.5%
 
G50191.5%
 
d46991.4%
 
V22470.7%
 
t21130.6%
 
S15450.5%
 
m8870.3%
 
M8750.3%
 
C6400.2%
 
Other values (4)19390.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
1545100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII34153799.8%
 
None6400.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o5838217.1%
 
r3658610.7%
 
e315689.2%
 
N274638.0%
 
g269467.9%
 
l239967.0%
 
a166834.9%
 
A128963.8%
 
z120093.5%
 
u120093.5%
 
n118353.5%
 
B97732.9%
 
c96492.8%
 
j94282.8%
 
R88192.6%
 
i75752.2%
 
s50511.5%
 
G50191.5%
 
d46991.4%
 
V22470.7%
 
t21130.6%
 
S15450.5%
 
15450.5%
 
m8870.3%
 
M8750.3%
 
Other values (4)19390.6%
 

Most frequent None characters

ValueCountFrequency (%) 
é640100.0%
 

Arma Medio
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
Llave maestra
27850 
Arma de fuego
24760 
Sin dato
9540 
No
5394 
Objeto contundente
 
1328
Other values (2)
 
1064
ValueCountFrequency (%) 
Llave maestra2785039.8%
 
Arma de fuego2476035.4%
 
Sin dato954013.6%
 
No53947.7%
 
Objeto contundente13281.9%
 
Arma cortopunzante9261.3%
 
Escopolamina1380.2%
 
2021-02-08T18:29:26.784032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-02-08T18:29:26.847016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:26.952400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length13
Mean length11.62871768
Min length2

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a11997814.8%
 
e11013013.5%
 
8916411.0%
 
r544626.7%
 
m536746.6%
 
o444785.5%
 
t432265.3%
 
d356284.4%
 
l279883.4%
 
s279883.4%
 
L278503.4%
 
v278503.4%
 
u270143.3%
 
A256863.2%
 
f247603.0%
 
g247603.0%
 
n155141.9%
 
i96781.2%
 
S95401.2%
 
N53940.7%
 
c23920.3%
 
O13280.2%
 
b13280.2%
 
j13280.2%
 
p10640.1%
 
Other values (2)10640.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter65416680.4%
 
Space Separator8916411.0%
 
Uppercase Letter699368.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L2785039.8%
 
A2568636.7%
 
S954013.6%
 
N53947.7%
 
O13281.9%
 
E1380.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a11997818.3%
 
e11013016.8%
 
r544628.3%
 
m536748.2%
 
o444786.8%
 
t432266.6%
 
d356285.4%
 
l279884.3%
 
s279884.3%
 
v278504.3%
 
u270144.1%
 
f247603.8%
 
g247603.8%
 
n155142.4%
 
i96781.5%
 
c23920.4%
 
b13280.2%
 
j13280.2%
 
p10640.2%
 
z9260.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
89164100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin72410289.0%
 
Common8916411.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a11997816.6%
 
e11013015.2%
 
r544627.5%
 
m536747.4%
 
o444786.1%
 
t432266.0%
 
d356284.9%
 
l279883.9%
 
s279883.9%
 
L278503.8%
 
v278503.8%
 
u270143.7%
 
A256863.5%
 
f247603.4%
 
g247603.4%
 
n155142.1%
 
i96781.3%
 
S95401.3%
 
N53940.7%
 
c23920.3%
 
O13280.2%
 
b13280.2%
 
j13280.2%
 
p10640.1%
 
z9260.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
89164100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII813266100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a11997814.8%
 
e11013013.5%
 
8916411.0%
 
r544626.7%
 
m536746.6%
 
o444785.5%
 
t432265.3%
 
d356284.4%
 
l279883.4%
 
s279883.4%
 
L278503.4%
 
v278503.4%
 
u270143.3%
 
A256863.2%
 
f247603.0%
 
g247603.0%
 
n155141.9%
 
i96781.2%
 
S95401.2%
 
N53940.7%
 
c23920.3%
 
O13280.2%
 
b13280.2%
 
j13280.2%
 
p10640.1%
 
Other values (2)10640.1%
 

Interactions

2021-02-08T18:29:09.163900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:09.283283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:09.395957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:09.506610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:09.621236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:09.747936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:09.862785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:09.984158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.107636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.218056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.318241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.416994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.523015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.629033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.740761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.857659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:10.971295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.084245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.187541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.284816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.388194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.494408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.596872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.706064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.811059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:11.925706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.032320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.137204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.248197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.471595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.582402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.700127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.815008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:12.930611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.040288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.145997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.258745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.373670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.487523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.603154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.721968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.846573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:13.954394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.061132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.172598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.285496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.396868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.512541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.626300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.756279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.878149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:14.998192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:15.124989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:15.246540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:15.362807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:15.482565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:15.711230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:15.829875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:15.939790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:16.048838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:16.163440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:16.279327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:16.394875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:16.512801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-08T18:29:27.039969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-08T18:29:27.179528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-08T18:29:27.318475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-08T18:29:27.490015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-08T18:29:27.748466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-08T18:29:17.022382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-08T18:29:18.121271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

AñoDíaDía nombreMes CalendarioJornadaBienCategoría BienGrupo BienConducta EspecialConductaModalidadSexoEdad HurtoRango EdadBarrionombre_barrioComunanombre_comunaPlana X HurtoPlana Y HurtoGeo X HurtoGeo Y HurtoCuadranteLugarGrupo LugarValor HurtoModelo HurtoLinea Marca HurtoMedio TransporteMedio Transporte AgresorSede ReceptoraMarcaColorArma Medio
020032003-01-01MiércolesEneroMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoAtracoHombre3633-38#0509Girardot55 Castilla834912.97151187879.789-75.5691266.293005MEVALPNVCCD02E01000003Vehículo particularTransporte18000001996LINEA STANDARDSin datoSin datoCastillaYamahaBlancoArma de fuego
120032003-01-01MiércolesEneroMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoAtracoHombre2929-32#0710López de Mesa77 Robledo-1.0000-1.000-1.000000-1.000000SIN DATOVía públicaTransporte10000001992AX 100Sin datoSin datoCastillaSuzukiGrisArma de fuego
220032003-01-01MiércolesEneroMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoHombre2318-24#0714Bello Horizonte77 Robledo-1.0000-1.000-1.000000-1.000000SIN DATOResidenciaHabitacional17000001995KTZ-100Sin datoSin datoCastillaAutecoGrisSin dato
320032003-01-01MiércolesEneroMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoHombre2318-24#0714Bello Horizonte77 Robledo-1.0000-1.000-1.000000-1.000000SIN DATOResidenciaHabitacional17000001996KTZ-100Sin datoSin datoCastillaAutecoGrisSin dato
420032003-01-01MiércolesEneroMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoSin datoHombre2318-24#0714Bello Horizonte77 Robledo-1.0000-1.000-1.000000-1.000000SIN DATOResidenciaHabitacional17000001996SIN DATOSin datoSin datoSin datoAutecoGrisSin dato
520032003-01-01MiércolesEneroMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoSin datoHombre2929-32#0710López de Mesa77 Robledo-1.0000-1.000-1.000000-1.000000SIN DATOVía públicaTransporte10000001992SIN DATOSin datoSin datoSin datoSuzukiGrisArma de fuego
620032003-01-01MiércolesEneroMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoAtracoHombre2018-24#Inst_4Terminal de Transporte55 Castilla834451.50701186034.474-75.5732956.276322MEVALPNVCCD02E01000017Vía públicaTransporte20000001982125 CCSin datoSin datoCastillaKawasakiGrisArma de fuego
720032003-01-01MiércolesEneroMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoSin datoHombre2318-24#0714Bello Horizonte77 Robledo-1.0000-1.000-1.000000-1.000000SIN DATOResidenciaHabitacional17000001995SIN DATOSin datoSin datoSin datoAutecoGrisSin dato
820032003-01-01MiércolesEneroMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoSin datoHombre2018-24#Inst_4Terminal de Transporte55 Castilla834451.50701186034.474-75.5732956.276322MEVALPNVCCD02E01000017Vía públicaTransporte20000001982SIN DATOSin datoSin datoSin datoKawasakiGrisArma de fuego
920032003-01-01MiércolesEneroMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoSin datoHombre3633-38#0509Girardot55 Castilla834912.97151187879.789-75.5691266.293005MEVALPNVCCD02E01000003Vehículo particularTransporte18000001996SIN DATOSin datoSin datoSin datoYamahaBlancoArma de fuego

Last rows

AñoDíaDía nombreMes CalendarioJornadaBienCategoría BienGrupo BienConducta EspecialConductaModalidadSexoEdad HurtoRango EdadBarrionombre_barrioComunanombre_comunaPlana X HurtoPlana Y HurtoGeo X HurtoGeo Y HurtoCuadranteLugarGrupo LugarValor HurtoModelo HurtoLinea Marca HurtoMedio TransporteMedio Transporte AgresorSede ReceptoraMarcaColorArma Medio
6992620202020-12-30MiércolesDiciembreNocheMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoHombre3333-38#1018Villa Nueva1010 La Candelaria835419.5749171.183455e+06-75.5645176.253062MEVALPNVCCD03E01000028Vía públicaTransporte1000000-1FZ16CaminataCaminataCandelariaYamahaAzulNo
6992720202020-12-30MiércolesDiciembreNocheMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoAtracoMujer3129-32#0717Robledo77 Robledo831891.1326221.186077e+06-75.5963966.276766MEVALPNVCCD02E01000039Vía públicaTransporte1000000-1GPD150-A (NMAX)CaminataCaminataCastillaYamahaNegroArma de fuego
6992820202020-12-31JuevesDiciembreTardeMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoHombre2018-24#0210Santa Cruz22 Santa Cruz836225.5239721.187989e+06-75.5572356.294046MEVALPNVCCD01E03000007ResidenciaHabitacional3000000-1BOXER 100 CTCaminataCaminataSanta CruzAutecoGrisNo
6992920202020-12-31JuevesDiciembreMadrugadaMotoVehículos de 2 o 4 ruedasVehículoNoHurto de motoHaladoHombre2625-28#0605La Esperanza66 Doce de Octubre834075.3737961.187658e+06-75.5766626.291055MEVALPNVCCD02E02000021Vía públicaTransporte1000000-1PULSAR 200 NSCaminataCaminataDoce de OctubreBajajNegroNo
6993020202020-12-31JuevesDiciembreMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoAtracoHombre5453-59#1007Guayaquil1010 La Candelaria834297.3365571.182723e+06-75.5746566.246445MEVALPNVCCD03E01000032Vía públicaTransporte2000000-1AK 125CaminataCaminataCandelariaAKTRojoArma cortopunzante
6993120202020-12-31JuevesDiciembreMañanaMotoVehículos de 2 o 4 ruedasVehículoNoHurto de motoHaladoHombre2525-28#0514Alfonso López55 Castilla834330.8269291.186850e+06-75.5743546.283754MEVALPNVCCD02E01000030Vía públicaTransporte45000002014PULSAR 200 NSCaminataCaminataCastillaBajajNegroNo
6993220202020-12-31JuevesDiciembreMadrugadaMotoVehículos de 2 o 4 ruedasVehículoNoHurto de motoHaladoHombre2929-32#0813Villatina88 Villa Hermosa837898.6289881.181803e+06-75.5421216.238124MEVALPNVCCD03E02000005Vía públicaTransporte60000002014PULSAR 200 NSCaminataCaminataVilla HermosaBajajBlancoNo
6993320202020-12-31JuevesDiciembreNocheMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoHombre4339-45#0107San Pablo11 Popular837634.3102521.187106e+06-75.5445076.286063MEVALPNVCCD01E02000019Vía públicaTransporte1000000-1125 NKDCaminataCaminataPopularAKTNegroNo
6993420202020-12-31JuevesDiciembreMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoMujer3939-45#1306La Pradera1313 San Javier829897.3075141.184222e+06-75.6144086.259994MEVALPNVCCD04E02000013Hotel, motel y hostalHabitacional1000000-1DH 125CaminataCaminataSan JavierYamahaGrisNo
6993520202020-12-31JuevesDiciembreMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoAtracoHombre3733-38#0206Andalucía22 Santa Cruz836443.0326561.188480e+06-75.5552706.298486MEVALPNVCCD01E03000012Vía públicaTransporte1000000-1PLATINOCaminataCaminataSanta CruzAutecoNegroArma cortopunzante

Duplicate rows

Most frequent

AñoDíaDía nombreMes CalendarioJornadaBienCategoría BienGrupo BienConducta EspecialConductaModalidadSexoEdad HurtoRango EdadBarrionombre_barrioComunanombre_comunaPlana X HurtoPlana Y HurtoGeo X HurtoGeo Y HurtoCuadranteLugarGrupo LugarValor HurtoModelo HurtoLinea Marca HurtoMedio TransporteMedio Transporte AgresorSede ReceptoraMarcaColorArma Mediocount
747720112011-02-21LunesFebreroMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato4000000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra7
747820112011-02-21LunesFebreroNocheMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato4000000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra6
750020112011-05-27ViernesMayoNocheMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato3000000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra5
752320162016-07-18LunesJulioMañanaMotoVehículos de 2 o 4 ruedasVehículoNoHurto de motoEngañoHombre2825-28#1011Calle Nueva1010 La Candelaria834530.50041181837.548-75.5725496.238436MEVALPNVCCD03E01000033Local comercialEconómica4000000-1SIN LINEACaminataCaminataCandelariaAutecoNegroNo4
748020112011-02-22MartesFebreroNocheMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato4000000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra3
748120112011-02-23MiércolesFebreroMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato4000000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra3
748420112011-02-25ViernesFebreroMadrugadaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato4000000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra3
748520112011-02-25ViernesFebreroMañanaMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato4000000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra3
748620112011-02-25ViernesFebreroNocheMotoVehículos de 2 o 4 ruedasVehículoSin datoHurto de motoHaladoSin dato-1Sin datoSIN DATOSin datoSIN DATOSin dato-1.0000-1.000-1.000000-1.000000SIN DATOSin datoSin dato2500000-1SIN DATOCaminataCaminataCastillaSin datoSin datoLlave maestra3
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